Neural Network Experiments on Personal Computers and Workstations - Tapa blanda

Sinopsis

Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this software also permits combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, colour graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus. The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience" counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification and more. In addition, the book introduces a simple, integrated environment for programming, displays and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN. The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs.

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