Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neural networks for signal processing
Neural networks for signal processing
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
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The NeSS (Neural Systems Simulator) environment is presented in this paper: it is a exible software package which has been developed to support, analyze and model dynamic non-linear systems for prediction, system identification and control applications, by providing both classical and innovative approaches within a exible and high-level framework. The behavior of each system is easily defined in a graphic way by interconnecting parametrized atomic objects (e.g., algebraic functions and neural networks), whose behaviors can be either predefined or identified by means of a learning procedure. Neural networks play a relevant role in NeSS: rich and easily expandable libraries are given which support different neural structures and learning algorithms.