A Novel Probabilistic-PSO Based Learning Algorithm for Optimization of Neural Networks for Benchmark Problems

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Abstract: This paper approbates a modern and stentorian version of standard particle swarm optimization (PSO) for optimization of initial weights and biases for multi layer feed forward neural networks (MLFFNN) with back propagation (BP). The combination of probabilistic-PSO and MLFFNN sevenfold help in fast convergence of MLFFNN in assortment and sortilege to various benchmark problems by alienating the imperfection of backpropagation of being stuck at local minima or local maxima. The propane probabilistic-PSO differs from the standard PSO in velocity and position parameters. In velocity parameters only particle best value is make use of for cicerone the particle to gait towards the pursuit in the search space, while in standard PSO both particle best and global best values are considered for adjudging the new velocity of the particle. A new parameter introduced which called as the probability parameter (P0), which adjudges if the standard PSO is that instead of using same random number, different particles use different random numbers to soar in search space. The proposed method used to detect the initial weights and biases for MLFFNN with BP, once the optimum value for initial weights and biases estimated the MLFFNN then used for classification and sortilege of various neural network benchmark problems. The benchmarking databases for neural network contain various datasets from various different domains. All datasets represent sensible issue, which can call diagnosis tasks, and all the datasets consist of real world data. The results for accuracy of the proposed probabilistic-PSO method compared with existing methods.

WSEAS Transactions on Electronics, ISSN / E-ISSN: 1109-9445 / 2415-1513, Volume 7, 2016, Art. #10

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Sudhir G. Akojwar, Pravin R. Kshirsagar, "A Novel Probabilistic-PSO Based Learning Algorithm for Optimization of Neural Networks for Benchmark Problems," WSEAS Transactions on Electronics, vol. 7, pp. 79-84, 2016, DOI:
Sudhir G. Akojwar, Pravin R. Kshirsagar. A Novel Probabilistic-PSO Based Learning Algorithm for Optimization of Neural Networks for Benchmark Problems. WSEAS Transactions on Electronics. 2016;7:79-84.
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