Biomimicry for Optimization, Control, and AutomationBiomimicry uses our scienti?c understanding of biological systems to exploit ideas from nature in order to construct some technology. In this book, we focus onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of biological systems for the development of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated levels of automation. The primary focus is not on the modeling, emulation, or analysis of some biological system. The focus is on using “bio-inspiration” to inject new ideas, techniques, and perspective into the engineering of complex automation systems. There are many biological processes that, at some level of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose automatic control, decision making, or automation. For instance, at the level of everyday experience, we can view the actions of a human operator of some process (e. g. , the driver of a car) as being a series of the best choices he or she makes in trying to achieve some goal (staying on the road); emulation of this decision-making process amounts to modeling a type of biological optimization and decision-making process, and implementation of the resulting algorithm results in “human mimicry” for automation. There are clearer examples of - ological optimization processes that are used for control and automation when you consider nonhuman biological or behavioral processes, or the (internal) - ology of the human and not the resulting external behavioral characteristics (like driving a car). For instance, there are homeostasis processes where, for instance, temperature is regulated in the human body. |
What people are saying - Write a review
We haven't found any reviews in the usual places.
Contents
Chapter Contents | 9 |
Scientific Foundations for Biomimicry | 56 |
Chapter Contents | 59 |
For Further Study | 95 |
Attentional Systems 263 | 103 |
Neural Network Substrates for Control Instincts | 105 |
Chapter Contents | 107 |
tron | 126 |
Adaptive Control | 547 |
Chapter Contents | 549 |
For Further Study | 601 |
The Genetic Algorithm 613 | 608 |
Chapter Contents | 615 |
Stochastic and Nongradient Optimization for Design 647 | 646 |
Chapter Contents | 649 |
Synergistic Effects | 718 |
RuleBased Control | 153 |
Chapter Contents | 155 |
Planning Systems 225 | 207 |
Planning Systems | 224 |
Chapter Contents | 227 |
Attentional Systems 263 | 262 |
Chapter Contents | 265 |
For Further Study | 309 |
Linear Least Squares Methods 421 | 420 |
Chapter Contents | 423 |
Gradient Methods | 470 |
Chapter Contents | 475 |
Chapter Contents | 721 |
3 | 735 |
For Further Study 755 | 754 |
Cooperative Foraging and Search 765 | 762 |
Chapter Contents | 768 |
Competitive and Intelligent Foraging | 829 |
Chapter Contents | 831 |
For Further Study | 895 |
899 | |
922 | |
Other editions - View all
Common terms and phrases
achieve adaptive control approach approximation error approximator structure attentional strategy automation bacteria basis function neural behavior biological biomimicry choice choose closed-loop complex consider control system cost function define defuzzification Design Problem desired ship discussed dynamics environment Equation error evolution example focus foraging function neural network fuzzy controller fuzzy set genetic algorithm goal hence hierarchical human implement initial intelligent control iteration learning linear mapping membership functions methodology model predictive control multilayer perceptron neural controller neurons nonlinear operation optimization organism output parameters performance PID controllers planning plant plot predator/prey radial basis function receptive field units recursive least squares represent response surface rudder rule base ship heading ship steering shown in Figure simply simulation specify step Suppose swarm Takagi-Sugeno fuzzy system tanker ship training data tuning values vector vehicle zero