Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Practical genetic algorithms
Fixed-order H2 and H∞ optimal deconvolution filter designs
Signal Processing
Fundamentals of Digital Signal Processing with Cdrom
Fundamentals of Digital Signal Processing with Cdrom
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computer Controlled Systems: Theory and Design
Computer Controlled Systems: Theory and Design
Blind linear channel estimation using genetic algorithm and SIMO model
Signal Processing
Blind adaptation of stable discrete-time IIR filters in state-space form
IEEE Transactions on Signal Processing
A Structural View of Stability in Adaptive IIR Filters
IEEE Transactions on Signal Processing
Design of IIR digital filters in the complex domain by transforming the desired response
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
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This paper proposes a novel evolution strategy for a genetic algorithm (GA). This new algorithm is then applied to design robust D (α, γ) -stable infinite-impulse-response (IIR) filters. Unlike existing research on designing IIR filters by using GA, in which the stability of IIR filters is tested by trial and error after the evolution of each generation of a GA, the stability criterion in this paper is embedded within the evolution of each generation. Consequently, the stability of this system can be guaranteed without the need for any other checks of the stability criterion in the evolution of each generation. Numerical experimental results are discussed to illustrate the soundness of the proposed evolution strategy. The robustness of the IIR filters is achieved by ensuring that all poles of the filters are located inside a disk D (α, γ) contained in the unit circle, in which α is the center, γ is the radius of the disk and |α| + γ 1. So, in this paper, a D (α, γ)-stability criterion will be first derived and then embedded in the GA for the design of robust DR filters. Finally, two examples will be presented to show that the designed filters remain D (α, γ) -stable during the evolution of the GA and will provide satisfactory results.