scholarly journals Improved Chaotic Quantum-Behaved Particle Swarm Optimization Algorithm for Fuzzy Neural Network and Its Application

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuexi Peng ◽  
Kejun Lei ◽  
Xi Yang ◽  
Jinzhang Peng

Traditional fuzzy neural network has certain drawbacks such as long computation time, slow convergence rate, and premature convergence. To overcome these disadvantages, an improved quantum-behaved particle swarm optimization algorithm is proposed as the learning algorithm. In this algorithm, a new chaotic search is introduced, and benchmark function experiments prove it outperforms the other five existing algorithms. Finally, the proposed algorithm is presented as the learning algorithm for Takagi–Sugeno fuzzy neural network to form a new neural network, and it is utilized in the water quality evaluation of Dongjiang Lake of Hunan province. Simulation results demonstrated the effectiveness of the new neural network.

Author(s):  
Pooja Rani ◽  
GS Mahapatra

This article develops a particle swarm optimization algorithm based on a feed-forward neural network architecture to fit software reliability growth models. We employ adaptive inertia weight within the proposed particle swarm optimization in consideration of learning algorithm. The dynamic adaptive nature of proposed prior best particle swarm optimization prevents the algorithm from becoming trapped in local optima. These neuro-prior best particle swarm optimization algorithms were applied to a popular flexible logistic growth curve as the [Formula: see text] model based on the weights derived by the artificial neural network learning algorithm. We propose the prior best particle swarm optimization algorithm to train the network for application to three different software failure data sets. The new search strategy improves the rate of convergence because it retains information on the prior particle, thereby enabling better predictions. The results are verified through testing approaching of constant, modified, and linear inertia weight. We assess the fitness of each particle according to the normalized root mean squared error which updates the best particle and velocity to accelerate convergence to an optimal solution. Experimental results demonstrate that the proposed [Formula: see text] model based prior best Particle Swarm Optimization based on Neural Network (pPSONN) improves predictive quality over the [Formula: see text], [Formula: see text], and existing model.


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