Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Stochastic Linear Quadratic Regulators with Indefinite Control Weight Costs
SIAM Journal on Control and Optimization
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Solving differential equations with genetic programming
Genetic Programming and Evolvable Machines
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Evolving control laws for a network of traffic signals
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genotype representations in grammatical evolution
Applied Soft Computing
Neural-network-based optimal fuzzy controller design for nonlinear systems
Fuzzy Sets and Systems
Application of advanced Grammatical Evolution to function prediction problem
Advances in Engineering Software
IEEE Transactions on Evolutionary Computation
Switching control of an R/C hovercraft: stabilization and smoothswitching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stable and optimal fuzzy control of linear systems
IEEE Transactions on Fuzzy Systems
Optimal fuzzy controller design: local concept approach
IEEE Transactions on Fuzzy Systems
Optimal fuzzy controller design in continuous fuzzy system: global concept approach
IEEE Transactions on Fuzzy Systems
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In this paper, optimal control for stochastic linear singular Takagi-Sugeno (T-S) fuzzy delay system with quadratic performance is obtained using genetic programming (GP). To obtain the optimal control, the solution of matrix Riccati differential equation (MRDE) is computed by solving differential algebraic equation (DAE) using a novel and nontraditional GP approach. The GP solution is equivalent or very close to the exact solution of the problem. Accuracy of the GP solution to the problem is qualitatively better. The solution of this novel method is compared with the traditional Runge Kutta (RK) method. An illustrative numerical example is presented for the proposed method.