PSO Based Neuro-fuzzy Model for Secondary Structure Prediction of Protein

Akbar, Sana; Pardasani, Kamal Raj; Panda, Nihar Ranjan

doi:10.1007/s11063-021-10615-6

Cited by 4 publications

(1 citation statement)

References 67 publications

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“…As a result of works carried out by many scientists and specialists all over the world over a dozen years, many methods, and algorithms for solving the problem of synthesis of control systems of electric power objects have been created. These include methods of classical [2], [3], heuristic [4]- [6] and artificial computational intelligence as ant Int J Elec & Comp Eng ISSN: 2088-8708  algorithms [7], artificial neural networks [8], fuzzy logics [9], cuckoo search, bee algorithms [10], particle swarm optimization [11], and evolutionary algorithms [12]. Nowadays, many of them are used to solve production problems.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of the neuro-fuzzy regulator with genetic algorithm

Siddikov,

Porubay,

Rakhimov

2024

IJECE

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Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data.

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