Multidimensional Neural Networks: Unified Theory

The book ''Multidimensional Neural Networks (MDNNs): Unified Theory'' has been conceived for serving 3 types of users: Senior undergraduate/graduate students, practising engineers, and advanced neural network researchers. This book is based on the following innovations: Multidimensional (M-D) logic theory i.e., conceiving logic gates/circuits operating on multidimensional arrays Tensor state space representation of certain M-D systems Relation M-D logic gates, M-D codeword tensors, M-D optimal control tensors to M-D neural networks unification Novel complex valued associative memory (CVNN) on the hypercube Novel models of biological neurons such as those with a linear filter model of synapse Neural network based signal processing The subject of M-D neural networks will have the applications in: Design of versatile associative memories, Optimal design of intelligent systems, Pattern recognition systems etc. Contents: Introduction Multi/Infinite Dimensional Neural Networks, Multi/Infinite Dimensional Logic Theory Multi/Infinite Dimensional Coding Theory: Multi/Infinite Dimensional Neural Networks?Constrained Static Optimization Tensor State Space Representation: Multi Dimensional Systems Unified Theory of Control, Communication and Computation: Multi Dimensional Neural Networks Complex Valued Neural Associative Memory on the Complex Hypercube Optimal Binary Filters: Neural Networks Linear Filter Model of a Synapse: Associated Novel Real/Complex Valued Neural Networks Novel Complex Valued Neural Networks Advanced Theory of Evolution of Living Systems