Volume Six: Recent Advances in Dynamics and Control of Neural Networks
E. Kaslik and S. Sivasundaram
Recent advances in dynamics and control of neural networks comprises research papers contributed by expert authors from various fields and explores many new ideas, results and directions in the rapidly growing field of dynamics and control of neural networks
2021 Hbk ISBN: 978-1-908106-16-2 250pp
It encompasses a wide range of topics including impulsive Cohen-Grossberg neural networks, dual neural network based adaptive controller, synthesis for aircraft with model uncertainties, discrete-time RTD-based cellular neural network.
The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either actual hardware or an algorithm, whose design was motivated by the design and functioning of human brains and components. There are several different types of neural networks, each of which has different strengths particular to their applications. The abilities of different networks can be related to their structure, dynamics, learning methods and control. Neural networks offer improved performance over conventional technologies in areas which include: adaptive control, optimization and scheduling, complex mapping, synchronization, machine vision, robust pattern detection, signal filtering, virtual reality, data segmentation, data compression, data mining, text mining, artificial life, flexible space systems etc. In general overall design process for aerospace systems typically consists of the following steps: design, analysis, and evaluation. Dynamics and Controls analyses, which define the critical performance of any aerospace system, are particularly important.
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