RANDOM NEURAL NETWORK LEARNING HEURISTICS

Author:

Javed Abbas,Larijani HadiORCID,Ahmadinia Ali,Emmanuel Rohinton

Abstract

The random neural network (RNN) is a probabilitsic queueing theory-based model for artificial neural networks, and it requires the use of optimization algorithms for training. Commonly used gradient descent learning algorithms may reside in local minima, evolutionary algorithms can be also used to avoid local minima. Other techniques such as artificial bee colony (ABC), particle swarm optimization (PSO), and differential evolution algorithms also perform well in finding the global minimum but they converge slowly. The sequential quadratic programming (SQP) optimization algorithm can find the optimum neural network weights, but can also get stuck in local minima. We propose to overcome the shortcomings of these various approaches by using hybridized ABC/PSO and SQP. The resulting algorithm is shown to compare favorably with other known techniques for training the RNN. The results show that hybrid ABC learning with SQP outperforms other training algorithms in terms of mean-squared error and normalized root-mean-squared error.

Publisher

Cambridge University Press (CUP)

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Statistics, Probability and Uncertainty,Statistics and Probability

Reference86 articles.

1. Experiments with Smart Workload Allocation to Cloud Servers

2. Constrained particle swarm optimization using a bi-objective formulation

3. Using artificial bee colony algorithm for mlp training on earthquake time series data prediction;Shah;Journal of Computing,2011

4. An Intelligent Internet Search Assistant Based on the Random Neural Network

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