Matlab Code forKevin M. Passino
Biomimicry for Optimization, Control, and Automation,
Springer-Verlag, London, UK, 2005
Biomimicry for Optimization, Control, and Automation,
Springer-Verlag, London, UK, 2005
The following is Matlab code that solves examples given in the book: Biomimicry for Optimization, Control, and Automation, by: K. Passino, the web site of which you can go to by clicking here.
Elements of Decision-Making:- Multilayer perceptron for tanker ship heading regulation, click here.
- Radial basis function neural network for tanker ship heading regulation, click here.
- Fuzzy controller for tanker ship heading regulation, click here.
- Path planning for autonomous vehicle guidance, click here, here, and here.
- Nonlinear model predictive control (planning) for level control in a surge tank, click here.
- Attentional strategies for dynamically focusing on multiple predators/prey, click here.
- Neural/fuzzy approximator construction basics, via an example unknown function, click here.
- Neural control (reinforcement learning) for tanker heading, click here.
- Neural control for tanker, only 9 receptive field units, click here.
- Fuzzy model reference learning control for a tanker ship, click here.
- Batch least squares for training a multilayer perceptron, click here.
- Batch least squares for training a Takagi-Sugeno fuzzy system, click here.
- Controller design using operator data, click here and here for the data set.
- Recursive least squares for training a multilayer perceptron, click here.
- Recursive least squares for training a Takagi-Sugeno fuzzy system, click here.
- Gas furnace data for estimation problem, click here.
- Steepest descent gradient method for on-line training a multilayer perceptron, click here.
- Levenberg-Marquardt method for training a Takagi-Sugeno fuzzy system, click here.
- Training a multilayer perceptron with the Matlab Neural Networks Toolbox, click here.
- Fuzzy c-means clustering and least squares for training an approximator, click here.
- Indirect neural control for a process control problem, click here.
- Direct neural control for a process control problem, click here.
- Stable indirect adaptive neural/fuzzy control for aircraft wing-rock regulation, click here and here.
- Stable direct adaptive neural/fuzzy control for aircraft wing-rock regulation, click here and here.
- Genetic algorithm (base-10), click here.
- Genetic algorithm approximations, click here.
- Topographical data for Colombia, optimization problem, click here and here.
- Response surface methodology for PD controller design for tanker ship, click here and here.
- Coordinate search, click here and here for the function to be optimized.
- Nelder-Mead simplex method for direct search, click here and here for the function to be optimized.
- Multidirectional search for optimization, click here, and here for the function to be optimized.
- Simultaneous perturbation stochastic approximation (SPSA), click here, and here for the function to be optimized.
- SPSA for PD controller design for a tanker ship, click here and here.
- SPSA for design of an attentional strategy, click here and here.
- Evolutionary design for a PD controller for a tanker ship, click here.
- Response surface methodology for approximator size choice, click here.
- Evolution and learning-instinct balance (study via estimator design), click here and here.
- Set-based stochastic optimization for evolving instinct-learning balance, click here.
- Indirect genetic adaptive control for a process control problem, click here.
- Social bacterial foraging for distributed optimization (E. coli based), click here, here, here, here, and here.
- Foraging strategy (E. coli) for indirect adaptive control for a process control problem, click here.
- Cooperative robot swarm obstacle avoidance problem, click here, here, here, and here.
- Simulation of Hamilton's selfish herd (object-oriented, in Matlab), click here.
- Simulation of distributed sychronization of fire fly flashes, click here and here.
- Security strategy solutions of bimatrix games, click here.
- Nash equilibria of bimatrix games, click here.
- Reaction curves and Nash equilibria, click here.
- Minimax strategy solutions for bimatrix games, click here.
- Stackelberg strategy solutions for bimatrix games, click here.
- Pareto optimal solutions, click here and here.
- Static foraging game example, click here.
Some C code (written by my graduate students):
- To download a fuzzy controller for an inverted pendulum coded in C, click here.
- To download a simulator for nonlinear systems based on the Runge-Kutta method (4th order) that is written in C and currently set up to simulate an inverted pendulum, click here (hence this code can be used together with the code for the fuzzy controller above to simulate a simple fuzzy control system).
- To download C code for a base-10 genetic algorithm that is currently configured to optimize a simple function, click here.
- For the programs written in C it is easy to output the data to a file and plot it in MATLAB. For a brief explanation of how to output data from programs and plot it in MATLAB, click here.
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