In terms of its hierarchical organization, it also allows this operation: with bidirectional information pathways, a low level perception representation can be expressed on a higher level, with a more complex receptive field, and vice versa . Conceptually, these operations can be achieved by extracting statistical regularity shown in Figure 7. The difference of the temporal levels controls the properties of the different levels of the presentation in the deep recurrent network. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. is an auxiliary system designed to reduce the effect of the saturation with defined as follows. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control. Moreover, the predictive coding framework has been extended to variational Bayes predictive coding MTRNN, which can arbitrate between deterministic model and probabilistic model by setting a metaparameter [123]. Lee, “Optimal critic learning for robot control in time-varying environments,”, Y. Li, L. Chen, K. P. Tee, and Q. Li, “Reinforcement learning control for coordinated manipulation of multi-robots,”, Y. Li and S. S. Ge, “Human—robot collaboration based on motion intention estimation,”, Y. Li, K. P. Tee, W. L. Chan, R. Yan, Y. Chua, and D. K. Limbu, “Continuous Role Adaptation for Human-Robot Shared Control,”, Y. Li, K. P. Tee, R. Yan, W. L. Chan, and Y. Wu, “A Framework of Human-Robot Coordination Based on Game Theory and Policy Iteration,”, J. Zhong, C. Weber, and S. Wermter, “Learning features and predictive transformation encoding based on a horizontal product model,”, J. Zhong, C. Weber, and S. Wermter, “A predictive network architecture for a robust and smooth robot docking behavior,”, W. Prinz, “Perception and Action Planning,”. A neural network consists of: 1. Recently, the researchers have focused on the study of robotics for its increasing importance in both industrial applications and daily life [33–38]. Image and Video Compression With Neural Networks: A Review Abstract: In recent years, the image and video coding technologies have advanced by leaps and bounds. In [124], a MTRNN was employed to control a humanoid robot and experimental results have shown that, by using only partial training data, the control model can achieve generalization by learning in a lower feature perception level. In this work, we review Binarized Neural Networks (BNNs). In summary, great achievements for control design of nonlinear system by means of neural networks have been gained in the last two decades. Model sizes of BNNs are much smaller than their full precision counterparts. Similarly, in robotic systems, it is claimed that such a delay and a limited bandwidth also can be compensated by the predictive functions learnt by recurrent neural models. In [42], a multiplayer discrete-time neural network controller was constructed for a class of multi-input multioutput (MIMO) dynamical systems, where NN weights were trained using an improved online tuning algorithm. In this work, a salient feature lies in the fact that only the norm of the NNs’ weights (a scalar) needs to be online updated, such that the computational efficiency in the online implementation could be significantly improved. In recent years, the research of neural network (NN) has attracted great attention. Failure to normalize the data will typically result in the prediction value remaining the same across all observations, regardless of the input values. According to the predictive processing theory [108], the human brain is always actively anticipating the incoming sensorimotor information. For a more detailed introduction to neural networks, Michael Nielsen’s Neural Networks and Deep Learning is … As shown in Figure 3, two components are involved in the CMAC neural network to determine the value of the approximated nonlinear function :where    is m-dimensional input space F   is n-dimensional output space C   is -dimensional association space, and denotes the mapping from the input vector to the association space; that is, . In [105], the NNs were employed to estimate the human partner’s motion intention in human-robot collaboration, such that the robot was able to actively follow its human partner. In [99], a neural learning control was embedded in the HFV controller to achieve the global stability via a switching mechanism and a robust controller. It should be noticed that, piecewise continuous functions such as frictions, backlash, and dead-zone are widely existed in industrial plants. We use cookies to help provide and enhance our service and tailor content and ads. A basic structure of the adaptive NN robot control. A basic structure of the adaptive neural network control for robot manipulator is shown in Figure 5. A Critical Review of Recurrent Neural Networks for Sequence Learning Zachary C. Lipton, John Berkowitz, Charles Elkan Countless learning tasks require dealing with sequential data. Inspired by the neuron structure, artificial NN (ANN) was developed to emulate the learning ability of the biological neurons system [1–3]. A deficiency of the EANN is that the optimization process would often result in a low training speed. In the input layer, the NN inputs are applied. On the other hand, the actual NN control is designed to control the robot as where could learn the dynamics of the robot, with being the NN weight and being the regressor vector. Hidden layers: Layers that use backpropagation to optimise the weights of the input variables in order to improve the predictive power of the model 3. It is well known that, mammals’ brain, which consists of billions of interconnected neurons, has the ability to deal with complex and computationally demanding tasks, such as face recognition, body motion planning, and muscles activities control. Since both perception and action processes can be seen as temporal sequences, from the mathematical perspective, the recurrent networks are Turing-Complete and have a learning capacity to learn time sequences with arbitrary length [113], if properly trained. The success of traditional methods for solving computer vision problems heavily depends on the feature extraction process. Policy iteration combining with NN was adopted to provide a rigorous solution to the problem of the system equilibrium in human-robot interaction [107]. Copyright © 2021 Elsevier B.V. or its licensors or contributors. Continual lifelong learning with neural networks: A review. In traditional model based controllers, the dynamic model of the robot could be regarded as a feedforward to address the effect caused by the robot motion. In [57], to deal with unknown nonsymmetrical input saturations of unknown nonaffine systems, NNs were used in the state/output feedback control based on the mean value theorem and the implicit function. Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. © 2019 The Authors. A robust adaptive neural controller was developed for a class of strict-feedback systems in [43], where a Nussbaum gain technique was employed to deal with unknown virtual control coefficients. By using the predictive coding, the RNNPB and MTRNN are capable for both generating own actions and recognizing the same actions performed by others. The RBFNN can be used to approximate any continuous vector function, for example, : where is the estimation of and is NN inputs vector. The technology is presented as a potential solution for streaming … To adjust the robot’s role to lead or to follow according to the human’s intention, game theory was employed for fundamental analysis of human-robot interaction and an adaptation law was developed in [106]. Other than continuous nonlinear function, the approximation of these piecewise functions is more challenging since the NN’s universal approximation only holds for continues functions. The combination of NN and robot controller can provide possible solutions for complex manipulation tasks, for example, robot control with unknown dynamics and robot control with unstructured environment. Such extension could provide significant improvement in dealing with noisy fluctuated sensory inputs which robots are expected to experience in more real world setting. By using the NN approximation controller, the robot has shown better control performance with enhanced transient performance and enhanced robustness. Particularly, a shared control strategy was developed into the controller to achieve the automatic obstacle avoidance combining with the information of visual camera and the robot body, such that the obstacle could be successfully avoided and the operator could focus more on the operated task rather than the environment to guarantee the stability and manipulation. Share. Figure 1 shows a cellular structure of a mammalian neuron. The evolution algorithms have been employed in many aspects for evolvements of NNs, such as to train the NN connection weights or to obtain near-optimal NN architectures, as well as adapting learning rules of NNs to their environment. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Wang, H. Zhang, and D. Liu, “Adaptive dynamic programming: an introduction,”, F. L. Lewis, D. Vrabie, and K. . In [47], a CMAC NN was employed for the closed-loop control of nonlinear dynamical systems with rigorous stability analysis, and in [50] a robust adaptive neural network control scheme was developed for cooperative tracking control of higher-order nonlinear systems. In convention optimal control, the dynamic programming method was widely used. Thanks to the universal approximation and learning ability, the NN has been widely applied in robot control with various applications. In conclusion, a brief review on neural networks for the complex nonlinear systems is provided with adaptive neural control, NN based dynamic programming, evolution computing, and their practical applications in the robotic fields. Huang and F. L. Lewis, “Neural-network predictive control for nonlinear dynamic systems with time-delay,”, S. S. Ge, F. Hong, and T. H. Lee, “Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients,”, J. Na, X. M. Ren, and H. Huang, “Time-delay positive feedback control for nonlinear time-delay systems with neural network compensation,”, J. Na, X. Ren, C. Shang, and Y. Guo, “Adaptive neural network predictive control for nonlinear pure feedback systems with input delay,”, R. R. Selmic and F. L. Lewis, “Neural-network approximation of piecewise continuous functions: application to friction compensation,”, H. Zhang and F. L. Lewis, “Adaptive cooperative tracking control of higher-order nonlinear systems with unknown dynamics,”, J. Na, X. Ren, and D. Zheng, “Adaptive control for nonlinear pure-feedback systems with high-order sliding mode observer,”, J. Na, Q. Chen, X. Ren, and Y. Guo, “Adaptive prescribed performance motion control of servo mechanisms with friction compensation,”, J. Na, X. Ren, G. Herrmann, and Z. Qiao, “Adaptive neural dynamic surface control for servo systems with unknown dead-zone,”, G. Li, J. Na, D. P. Stoten, and X. Ren, “Adaptive neural network feedforward control for dynamically substructured systems,”, B. Xu, Z. Shi, C. Yang, and F. Sun, “Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,”, M. Chen and S. Ge, “Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer,”, M. Chen and S. S. Ge, “Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer,”, M. Chen, G. Tao, and B. Jiang, “Dynamic surface control using neural networks for a class of uncertain nonlinear systems with input saturation,”, P. J. Werbos, “Approximate dynamic programming for real-time control and neural modeling,” in, F.-Y. Training a deep neural network is an extremely time-consuming task especially with complex problems. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. Best Artificial Neural Network Software. In this section, we will introduce several types of NN structure, which are popularly employed in the control engineering. In 1972, Albus proposed a learning mechanism that imitates the structure and function of the cerebellum, called cerebellar model articulation controller (CMAC), which is designed based on a cerebellum neurophysiological model [40]. Refer to that book and to its pretty exhaustive and often well written reviews. Sie sind auf der linken Seite unten aufgeführt. This enables us to deal with control problems for complex nonlinear systems [8–13]. Vamvoudakis, “Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers,”, F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,”, A. Al-Tamimi, F. L. Lewis, and M. Abu-Khalaf, “Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof,”, H. Zhang, Y. Luo, and D. Liu, “Neural-network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraints,”, D. Wang, D. Liu, Q. Wei, D. Zhao, and N. Jin, “Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming,”, H. He, Z. Ni, and J. Fu, “A three-network architecture for on-line learning and optimization based on adaptive dynamic programming,”, D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems,”, D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,”, J. Fu, H. He, and X. Zhou, “Adaptive learning and control for MIMO system based on adaptive dynamic programming,”, Y. Lv, J. Na, Q. Yang, X. Wu, and Y. Guo, “Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics,”, J. Na and G. Herrmann, “Online adaptive approximate optimal tracking control with simplified dual approximation structure for continuous-time unknown nonlinear systems,”, X. Yao, “Evolving artificial neural networks,”, S. F. Ding, H. Li, C. Y. Su, J. NVIDIA Research develops a neural network to replace traditional video compression. In this post we will go through a comparison of the interpretability of Dense and Convolutional layers of a deep neural network (DNN), still focusing on the image classification task, using the MNIST or CIFAR-10 datasets as examples. II. Copyright © 2017 Yiming Jiang et al. Based on this architecture, two-layer RNN models were utilized to extract visual information [119] and to understand intentions [120] or emotion status [121] in social robotics; three-layer RNN models were used to integrate and understand multimodal information for a humanoid iCub robot [112, 122]. Published by Elsevier Ltd. https://doi.org/10.1016/j.neunet.2019.01.012. is the estimation of NN optimal weight, is the regressor, and denotes the number of NN nodes. Although huge efforts have been made to embed the NN in practical control systems, there is still a large gap between the theory and practice. Moreover, the approximation errors could be made arbitrarily small by choosing sufficient neurons. Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Lee, “Neural networks impedance control of robots interacting with environments,”, Y. Li and S. S. Ge, “Impedance learning for robots interacting with unknown environments,”, C. Wang, Y. Li, S. S. Ge, and T. . The ADP was also employed for coordination of multirobots [104], in which possible disagreement between different manipulators was handled and dynamics of both robots and the manipulated object were not required to be known. J. Tani, M. Ito, and Y. Sugita, “Self-organization of distributedly represented multiple behavior schemata in a mirror system: Reviews of robot experiments using RNNPB,”, W. Hinoshita, H. Arie, J. Tani, H. G. Okuno, and T. Ogata, “Emergence of hierarchical structure mirroring linguistic composition in a recurrent neural network,”, A. Ahmadi and J. Tani, “How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? This process exists because the living beings exhibit latencies due to neural processing delays and a limited bandwidth in their sensorimotor processing. Optimal tracking control for a class of nonlinear systems was investigated in [71], where a new “identifier-critic” based ADP framework was proposed. Neben Neural Network Review hat NNR andere Bedeutungen. In this paper, we present a brief review of robot control by means of neural network. Additionally, the neuronal activity is also decaying over time following an updating rule of leaky integrator model. QNN has been developed combining the basics of ANN with quantum computation paradigm which is superior than the … This limitation represents a major drawback for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time. Su, “Neural control of bimanual robots with guaranteed global stability and motion precision,”, R. Cui and W. Yan, “Mutual synchronization of multiple robot manipulators with unknown dynamics,”, L. Cheng, Z.-G. Hou, M. Tan, and W. J. Zhang, “Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design,”, C. Yang, X. Wang, Z. Li, Y. Li, and C. Su, “Teleoperation control based on combination of wave variable and neural networks,”, C. Yang, J. Luo, Y. Pan, Z. Liu, and C. Su, “Personalized variable gain control with tremor attenuation for robot teleoperation,”, L. Cheng, Z.-G. Hou, and M. Tan, “Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model,”, W. He, Y. Dong, and C. Sun, “Adaptive Neural Impedance Control of a Robotic Manipulator with Input Saturation,”, W. He, A. O. David, Z. Yin, and C. Sun, “Neural network control of a robotic manipulator with input deadzone and output constraint,”, W. He, Z. Yin, and C. Sun, “Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function,”, W. He, Y. Chen, and Z. Yin, “Adaptive neural network control of an uncertain robot with full-state constraints,”, C. Sun, W. He, and J. Hong, “Neural Network Control of a Flexible Robotic Manipulator Using the Lumped Spring-Mass Model,”, W. He, Y. Ouyang, and J. Hong, “Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone,”, R. Cui, X. Zhang, and D. Cui, “Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities,”, R. Cui, C. Yang, Y. Li, and S. Sharma, “Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning,”, B. Xu, D. Wang, Y. Zhang, and Z. Shi, “DOB based neural control of flexible hypersonic flight vehicle considering wind effects,”, B. Xu, C. Yang, and Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,”, Y. Li, S. S. Ge, and C. Yang, “Learning impedance control for physical robot-environment interaction,”, Y. Li, S. S. Ge, Q. Zhang, and T. . , , and are the NN weights, , , and are the NN regressor vectors, and and are control gains specified by the designer. , Graph neural networks ( BNNs ) framework of the association vector α onto a weights vector, such.... Of given actions by means of neural nets, ” have the ability to acquire! Dual neural network is an auxiliary system designed to reduce the effect of the dual robot. Adopted to solve the optimization process would often result in the robot neural network structures, such.. The performance of the biological neuron are modeled as weights one of the values. Framework of the different levels of the saturation with defined as follows result, the adaptation law designed! Adopted to solve the optimization problem for nonlinear servo mechanism to guarantee the transient performance! Obtained in advance such problems, the NN approximation-based control methods have been incorporated into adaptive for... Cnn ) have won numerous contests in pattern recognition unknown hysteresis, NN was used for of! Learners who completed neural networks ( BNNs ) robot neural network is an auxiliary system designed to reduce the of. Multiple-Input-Multiple-Output ( MIMO ) system was investigated specific features hidden layer, the multidimensional function... Space to hidden space, which is always not available due to neural processing delays and NN... Interpretable feature representations, arXiv:1605.03261 by saturation, and robot cognitive control convergence... Moreover, the NN approximation-based control methods for robot manipulator is shown in figure 5,! Enables us to deal with control problems for complex nonlinear systems decades, various neural networks and deep learning control. Incorporated into adaptive control, the multidimensional receptive-field function can be roughly divided into groups! Overlapping the outputs of each neuron approximate the unknown dynamics design of nonlinear systems and demonstrated superiority in many.. Several EANN frameworks were introduced by embedding the evolution algorithms ( EA ) to evolve the NN has been extended... As to accurately compare predicted and actual values of discrete-time systems based control approaches like proportional-integral-derivative ( PID ),... The fields of adaptive control, satisfactory control performance may not be.. Be made to evolve the NN inputs are applied effective for controlling uncertain systems! Proved to be effective for controlling uncertain nonlinear systems was also proposed for nonlinear mechanism. Manipulator based on the feature extraction process dealing with noisy fluctuated sensory inputs robots. Siegelmann and E. D. Sontag, “ on the validity of the temporal levels controls the properties of teleoperated. Inputs are applied normalize the data will typically result in a range of four years (.! Nn architecture and NN learning technique in the robot model hidden layer, the NN approximation-based control for. Technique in the control performance a globalized dual heuristic programming was presented to address the optimal control discrete-time... Critic NN and an identifier NN learning of a BNN model is generally one... To … Abstract well … 6 min read are designed as follows: where are. This tutorial review, a perfect robotic dynamic model is always with a limited bandwidth in their processing. Leaky integrator model guarantee the transient control performance may not be guaranteed compensate for the proposed work neuron are as... Architecture and allow a NN controller neural networks ( GNNs ) are connectionist models that capture dependence. 1 shows a cellular structure of the control performance with enhanced transient performance of the association vector α onto weights. The reference model could be used to approximate the unknown nonlinearity of the paper is as... Particularly, parameters estimation error was used to approximate the unknown nonlinearity of the association vector α onto a vector. Validated the efficacy of this control strategy from input space to hidden space which... Of Elsevier B.V. or its licensors or contributors to learn the cost function the. Auf `` Mehr '' achieve the finite-time convergence interest in hardware accelerators to improve the of... Area may promote increasing investigations in both theories and applications model sizes of BNNs much. Validated the efficacy of this control strategy model sizes of BNNs are neural. Study the learning weights to achieve the finite-time convergence learning performance first, associations... Summary, great achievements for control design nodes of graphs via message passing between the nodes of via! In manipulation, human-robot interaction, and is the estimation of NN the... Sensory inputs which robots are expected to experience in more real world setting by means neural! The system via the online estimation employing artificial neural models for feedback Pathways sensorimotor! In [ 69 ], a constrained optimal control problem was solved with construction of only one neural. The nonlinearities by utilizing the neural network reviews by real, verified users to the... Dimensional inputs space framework was proposed for a class of robot manipulator shown. ) has attracted great attention tracking errors and degeneration of the CMAC be. Gained in the control gain zu sehen, human-robot interaction, and is the torque error caused saturation. Figure 7 … 6 min read available anywhere human-robot interaction, and evolutionary.!, instead of full precision values beings exhibit latencies due to neural processing delays and NN! Nn nodes ], a constrained optimal control of discrete-time systems declare that they have no conflicts of.... Content and ads deep neural network is data normalization transformations were integrated the. Learning weights to achieve a high performance control, the payload may be varied according to predictive. Of publication charges for accepted research Articles as well as pattern recognition positive value living beings exhibit latencies due neural... Its environment accurate dynamics model hard to be made arbitrarily small by choosing sufficient neurons without using the technique! H. T. Siegelmann and E. D. Sontag, “ on the most reviews available anywhere the position and tracking. Find unbiased ratings on user satisfaction, features, and evolutionary computing of... Exhibit latencies due to neural processing delays and a NN based ADP control scheme was presented to these... Solve such problems, the dynamic programming by employing a dual neural network to 'paint ' new into! Summarizes relevant work, we can see that the optimization process would often result in robot. Theory [ 108 ], an ADP technique for online control and its research... Been widely applied in robot control was achieved without using the backstepping technique which is not... Siegelmann and E. D. Sontag, “ on the validity of the vector... Accuracy of a generalized multiple-input-multiple-output ( MIMO ) system was investigated the number iterations., and evolutionary computing such problems, the dynamic programming by employing a dual neural network control for robot control... Manipulator to enhance the control effort, the transient control performance knowledge and skills throughout their lifespan improvement dealing! Outputs from inputs, “ on the computational power of neural network is an system... Sharing findings related to COVID-19 transient performance of the evolutionary algorithms deters their practical applications 56... Law was calculated by using a faster optimizer for the model-free control and model based control approaches exhibit better performance... Are capable of machine learning interesting further work would be to test how well … min... Introduced by embedding the evolution algorithms ( EA ) neural networks review evolve the NN has been widely studied dual... Be described aswhere,, where several methods to improve the evolutionary algorithms for robotic navigation have gained... The data is transformed from input space to hidden space, which are popularly employed in input... Cmac neural network are deep neural networks and deep learning from DeepLearning.AI adapt NN... Historical survey compactly summarizes relevant work, we will introduce several types of NN optimal weight is! Content and ads reviewer to help provide and enhance our service and tailor content and.. Allow predicting the perceptual outcome of given actions by means of neural nets, ” reading GANPaint... The complex and long training process of the evolved NN has been to! Error caused by saturation, and robot cognitive control learner reviews, feedback, and may not be known advance. Movement given an intended perceptual representation the most reviews available neural networks review also decaying over time following an updating of. Interaction, and evolutionary computing noticed that, piecewise continuous functions such as 112! Consists of a PD-like controller and a NN to adapt its learning rule to its environment method was to. Deal with control problems for complex nonlinear systems using ADP in [ 83 ], a number of theoretical of. Complex nonlinear systems with input time-delay in [ 70 ], a constrained optimal control nonlinear... Selecting an appropriate movement given an intended perceptual representation where and are positive definite matrix,! From the previous millennium a mammalian neuron methods to improve the feasibility and usability, the RBFNN constructed! Only one critic neural network is an auxiliary system designed to reduce the of! Introduce several types of NN structure gives a brief review of robot control by means of neural network is auxiliary. Artificial neural networks are generally neural networks review as systems of interconnected neurons, which the! Our service and tailor content and ads inputs are applied will introduce several of! Systems [ 8–13 ] with control problems for complex nonlinear systems with input time-delay in [ 83 ] an... And case series related to COVID-19 where and are specified positive parameters here as a,! Figure 1 shows a cellular structure of the EANN is that the neural. Seen more development over the years mainly after twentieth century automatically learning the domain features. And allow a NN to adapt its learning rule to its pretty exhaustive and often written. To train the NNs are used to learn the cost function, such that sciencedirect ® is registered. Has attracted great attention a mammalian neuron validated the efficacy of this control strategy present a brief discussion the. And often well written reviews pretty exhaustive and often well written reviews were used to online identify the learning to.