Applied combinatorics
Digital control system analysis and design (3rd ed.)
Digital control system analysis and design (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Spikes: exploring the neural code
Spikes: exploring the neural code
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Godel, Escher, Bach: An Eternal Golden Braid
Godel, Escher, Bach: An Eternal Golden Braid
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Inference Of Differential Equation Moels By Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Computational models for neuroscience: human cortical information processing
Computational models for neuroscience: human cortical information processing
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
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This paper extends research in the area of biologically inspired, discrete dynamical basis networks (DDBNs). While similar to locally recurrent globally feedforward (LRGF) networks (Tsoi and Back 1994), DDBNs operate at a lower level of abstraction and were inspired by research in the areas of Control Systems, Artificial Intelligence, and Neurobiology. As described previously, DDBNs can approximate data sequences and consist of networks of simple, bounded mathematical operators (Jones and Olmsted 2003). This paper examines the characteristics of genetically designed DDBNs and compares them with tree-based genetic programs (TBGPs), biological neural networks, and backpropagation neural networks (NNs). Experimental evidence indicates that DDBNs are capable of computing simple logic functions in addition to approximating data sequences.