Multilayer Neural Networks

Multilayer neural networks are typical learning systems. Here we can find all the necessary components of a learning system: a performance index, a memory, and learning algorithms. Being designed according to the principles of their biological analogues, multilayer neural networks (MNN) are able to solve a wide range of problems in pattern recognition [1], identification [2], control of complex dynamical non-linear systems [3], [4], robot control [5], etc.

[1]  R. H. White.  Recurrent competitive Hebbian learning , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[2]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[3]  R. Rescorla,et al.  A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .

[4]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[5]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[6]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[7]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[8]  D. Hebb.  Essay on mind , 1980 .

[9]  Richard S. Sutton,et al.  Neural networks for control , 1990 .

[10]  D. Sprecher.  On the structure of continuous functions of several variables , 1965 .

[11]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[12]  George W. Irwin,et al.  Neural networks for control and systems , 1992 .

[13]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[16]  Mo M. Jamshidi,et al.  Neural networks in robotics: A survey , 1990, J. Intell. Robotic Syst..

[17]  Stephen A. Billings,et al.  Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .

[18]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[19]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .