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The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development
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作者 Conrad Onésime Oboulhas Tsahat Ngoulou-A-Ndzeli Béranger Destin Ossibi 《Journal of Computer and Communications》 2024年第7期1-11,共11页
Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take ca... Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described. 展开更多
关键词 Multilayer neural network Multidimensional nonlinear Interpolation Generalization by Similarity Artificial Intelligence Prototype Development
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Adaptive output feedback control for nonlinear time-delay systems using neural network 被引量:9
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作者 Weisheng CHEN Junmin LI 《控制理论与应用(英文版)》 EI 2006年第4期313-320,共8页
This paper extends the adaptive neural network (NN) control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backsteppi... This paper extends the adaptive neural network (NN) control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples. 展开更多
关键词 Time delay nonlinear system neural network BACKSTEPPING Output feedback Adaptive control
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Nonlinear Dynamics and Stability of Neural Networks with Delay-Time 被引量:12
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作者 L. C. Jiao, member, IEEE, and Zheng Bao, Senior member, IEEECenter for Neural Networks and Institute of Elec. Eng, Xidian University, Xian 710071, China 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1992年第2期13-26,共14页
In this paper we study the dynamic properties and stabilities of neural networks with delay-time (which includes the time-varying case) by differential inequalities and Lyapunov function approaches. The criteria of co... In this paper we study the dynamic properties and stabilities of neural networks with delay-time (which includes the time-varying case) by differential inequalities and Lyapunov function approaches. The criteria of connective stability, robust stability, Lyapunov stability, asymptotic atability, exponential stability and Lagrange stability of neural networks with delay-time are established, and the results obtained are very useful for the design, implementation and application of adaptive learning neural networks. 展开更多
关键词 nonlinear dynamics STABILITY neural network.
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The adaptive control using BP neural networks for a nonlinear servo-motor 被引量:2
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作者 Xinliang ZHANG Yonghong TAN 《控制理论与应用(英文版)》 EI 2008年第3期273-276,共4页
The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathema... The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification (NNI) and control (NNC), respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness. 展开更多
关键词 Servo-motor nonlinearITY neural networks based control Lyapunov stability Learning rate
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Neural network based adaptive sliding mode control of uncertain nonlinear systems 被引量:4
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作者 Ghania Debbache Noureddine Goléa 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第1期119-128,共10页
The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activat... The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control (SMC). Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results. 展开更多
关键词 nonlinear system neural network sliding mode con- trol (SMC) adaptive control stability robustness.
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Adaptive H~∞ Control of Nonlinear Systems with Neural Networks 被引量:6
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作者 姜长生 陈谋 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2003年第1期36-41,共6页
The discussion is devoted to the adaptive H ∞ control method based on RBF neural networks for uncertain nonlinear systems in this paper. The controller consists of an equivalent controller and an H ∞ cont... The discussion is devoted to the adaptive H ∞ control method based on RBF neural networks for uncertain nonlinear systems in this paper. The controller consists of an equivalent controller and an H ∞ controller. The RBF neural networks are used to approximate the nonlinear functions and the approximation errors of the neural networks are used in the adaptive law to improve the performance of the systems. The H ∞ controller is designed for attenuating the influence of external disturbance and neural network approximation errors. The controller can not only guarantee stability of the nonlinear systems, but also attenuate the effect of the external disturbance and neural networks approximation errors to reach performance indexes. Finally, an example validates the effectiveness of this method. 展开更多
关键词 neural networks nonlinear systems adaptive control H control
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An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems 被引量:29
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作者 Hongjun Yang Jinkun Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第2期457-462,共6页
This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unkn... This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system. 展开更多
关键词 Index TermsbAdaptive control neural network (NN) nonlin-ear system radial basis function.
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Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models 被引量:8
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作者 Lianfei ZHAI Tianyou CHAI 《控制理论与应用(英文版)》 EI 2006年第1期62-69,共8页
For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a tra... For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm. 展开更多
关键词 nonlinear Decoupling control PID neural networks Multiple models Generalized minimum variance
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Evaluation on Stability of Stope Structure Based on Nonlinear Dynamics of Coupling Artificial Neural Network 被引量:7
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作者 Meifeng Cai Xingping Lai 《Journal of University of Science and Technology Beijing》 CSCD 2002年第1期1-4,共4页
The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activa... The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com- puting is a practical and advanced tool for solving large-scale underground rock engineering problems. 展开更多
关键词 coupling neural network nonlinear dynamics structural stability stope parameters
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Model algorithm control using neural networks for input delayed nonlinear control system 被引量:2
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作者 Yuanliang Zhang Kil To Chong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期142-150,共9页
The performance of the model algorithm control method is partially based on the accuracy of the system's model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. ... The performance of the model algorithm control method is partially based on the accuracy of the system's model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems. 展开更多
关键词 model algorithm control neural network nonlinear system time delay
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Adaptive RBF neural network control of robot with actuator nonlinearities 被引量:5
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作者 Jinkun LIU, Yu LU (School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China) 《控制理论与应用(英文版)》 EI 2010年第2期249-256,共8页
In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinear... In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion. 展开更多
关键词 Adaptive control RBF neural network Actuator nonlinearity Robot manipulator DEADZONE
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Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems 被引量:5
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作者 Chen Weisheng Li Junmin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2006年第3期611-618,共8页
For a class of unknown nonlinear time-delay systems, an adaptive neural network (NN) control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a r... For a class of unknown nonlinear time-delay systems, an adaptive neural network (NN) control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness (UUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example. 展开更多
关键词 nonlinear time-delay system neural network adaptive bounding technique memoryless adaptive NN controller.
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Adaptive Neural Network Dynamic Surface Control for Perturbed Nonlinear Time-delay Systems 被引量:4
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作者 Geng Ji 《International Journal of Automation and computing》 EI 2012年第2期135-141,共7页
This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown ... This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of "explosion of complexity" in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of "curse of dimensionality" and "explosion of complexity" are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach. 展开更多
关键词 Adaptive control dynamic surface control neural network nonlinear time delay system stability analysis.
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Design of performance robustness for uncertain nonlinear time-delay systems via neural network 被引量:2
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作者 Luan Xiaoli Liu Fei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第4期852-857,884,共7页
Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model t... Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities (LMIs). Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays. 展开更多
关键词 nonlinear system TIME-DELAY UNCERTAINTIES neural network linear matrix inequality
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Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation 被引量:7
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作者 Nassira Zerari Mohamed Chemachema Najib Essounbouli 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第1期278-290,共13页
In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach... In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller. 展开更多
关键词 Adaptive control INPUT SATURATION neural networks systems (NNs) nonlinear pure-feedback
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A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems 被引量:2
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作者 Li Shaoyuan & Xi Yugeng (Shanghai Jiaotong University, 200030, P. R. China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2000年第1期61-66,共6页
In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neu... In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications. 展开更多
关键词 Fuzzy logic neural networks Adaptive control nonlinear dynamic system.
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Adaptive Output-feedback Regulation for Nonlinear Delayed Systems Using Neural Network 被引量:9
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作者 Wei-Sheng Chen Jun-Min Li Department of Applied Mathematics,Xidian University,Xi′an 710071,PRC 《International Journal of Automation and computing》 EI 2008年第1期103-108,共6页
A novel adaptive neural network (NN) output-feedback regulation algorithm for a class of nonlinear time-varying timedelay systems is proposed. Both the designed observer and controller are independent of time delay.... A novel adaptive neural network (NN) output-feedback regulation algorithm for a class of nonlinear time-varying timedelay systems is proposed. Both the designed observer and controller are independent of time delay. Different from the existing results, where the upper bounding functions of time-delay terms are assumed to be known, we only use an NN to compensate for all unknown upper bounding functions without that assumption. The proposed design method is proved to be able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed system, and the system output is proved to converge to a small neighborhood of the origin. The simulation results verify the effectiveness of the control scheme. 展开更多
关键词 ADAPTIVE neural network (NN) OUTPUT-FEEDBACK nonlinear time-delay systems BACKSTEPPING
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Dynamic Neural Network Based Nonlinear Control of a Distillation Column 被引量:1
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作者 Feng Li 《Intelligent Control and Automation》 2011年第4期383-387,共5页
Taking advantage of the knowledge of top and bottom compositions of a distillation column, a dynamic neural network (DNN) is designed to identify the input-output relationship of the column. The weight-training algori... Taking advantage of the knowledge of top and bottom compositions of a distillation column, a dynamic neural network (DNN) is designed to identify the input-output relationship of the column. The weight-training algorithm is derived from a Lyapunov function. Based on this empirical model, a nonlinear H∞ controller is synthesized. The effectiveness of the control strategy is demonstrated using simulation results. 展开更多
关键词 DISTILLATION COLUMN DYNAMIC neural network nonlinear H∞ Control
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A New Class of Nonlinear Error Control Codes Based on Neural Networks 被引量:1
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作者 Jin Fan Fan Junbo Deng Xingming(School of Computer and Communicalion Engineering,Southwest Jiaolong University),Chengdu 610031, Chiua 《Journal of Modern Transportation》 1995年第2期109-116,共8页
By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also ... By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also given forillustrating this new approach. 展开更多
关键词 error control neural networks nonlinear codes
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A Back Propagation-Type Neural Network Architecture for Solving the Complete n ×n Nonlinear Algebraic System of Equations 被引量:1
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作者 Konstantinos Goulianas Athanasios Margaris +2 位作者 Ioannis Refanidis Konstantinos Diamantaras Theofilos Papadimitriou 《Advances in Pure Mathematics》 2016年第6期455-480,共26页
The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propaga... The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neurons of the second hidden layer. The paper presents the fundamental theory associated with this approach as well as a set of experimental results that evaluate the performance and accuracy of the proposed method against other methods found in the literature. 展开更多
关键词 nonlinear Algebraic Systems neural networks Back Propagation Numerical Analysis Computational Methods
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