Advanced Methods in Neural ComputingFollowing up where Neural Computing: Theory and Practice left off, this guide explains diverse high-performance paradigms for artificial neural networks (ANNs) that function effectively in real-world situations. The tutorial approach, use of standardized notation, undergraduate-level mathematics, and extensive examples explain methods for solving practical neural network engineering problems in a clear and comprehensible manner. Emphasis is given to paradigms that perform well rather than those of academic interest. Explanations of the paradigms are program-oriented and are written in algorithmic form. Self-contained chapters cover field theory methods, including Nestor's restricted coulomb energy system; probabilistic neural networks, which can increase training speed by orders of magnitude; genetic algorithms that mimic biological evolution; sparse distributed memory, a powerful associative memory paradigm, which is compatible with VLSI implementation; fuzzy logic methods that are finding widespread application in control systems; neural engineering, including a set of techniques for designing, training, and applying artificial neural systems to real-world problems; and additional chapters cover basis function methods, chaos, and automatic control. Most of the paradigms presented have been used by the author in actual applications. Paradigms that are still in the research stage, but offer great potential, are also discussed. Advanced Methods in Neural Computing meets the reference needs of electronics engineers, control systems engineers, programmers, and others in scientific disciplines. |
Contents
Preface | 1 |
Field Theory Methods | 14 |
Probabilistic Neural Networks | 35 |
Copyright | |
9 other sections not shown
Common terms and phrases
accuracy adaptive adjusting algorithm allows applications approach approximation artificial neural networks associated average backpropagation basin basis become binary calculated called chaotic characteristics charge classifier complexity components computation considered convergence decision defined described desired Despite determine developed dimension direction distance distribution effect Equation error estimate example experience field Figure function fuzzy genetic algorithms given hidden layer human important increase indicates input vector International layer neuron learning limited logic measure membership function memory method nature neuron nonlinear normalization Note objective operation optimal output paradigms pattern performance population position possible probability problem produce range reference region represent response rule selected separate shown shows similar simple single solution solve space step stored strings techniques theory tion training set training vector variables weights