PSO-based single multiplicative neuron model for time series prediction

Single multiplicative neuron model is a novel neural network model introduced recently, which has been used for time series prediction and function approximation. The model is based on a polynomial architecture that is the product of linear functions in different dimensions of the space. Particle swarm optimization (PSO), a global optimization method, is proposed to train the single neuron model in this paper. An improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. The proposed CRPSO, PSO, back-propagation algorithm and genetic algorithm are employed to train the model for three well-known time series prediction problems. The experimental results demonstrate the superiority of CRPSO-based neuron model in efficiency and robustness over the other three algorithms.

[1]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[4]  Kamel Mohamed,et al.  Cooperative Particle Swarm Optimizers: A Powerful and Promising Approach , 2006 .

[5]  Kaoru Hirota,et al.  A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron , 2004, Neural Processing Letters.

[6]  Prem Kumar Kalra,et al.  Time-series prediction with single integrate-and-fire neuron , 2007, Appl. Soft Comput..

[7]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Amir B. Geva,et al.  ScaleNet-multiscale neural-network architecture for time series prediction , 1998, IEEE Trans. Neural Networks.

[9]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[10]  Prem Kumar Kalra,et al.  Time series prediction with single multiplicative neuron model , 2007, Appl. Soft Comput..

[11]  Jacek M. Zurada,et al.  Bi-directional computing architecture for time series prediction , 2001, Neural Networks.

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Hiok Chai Quek,et al.  GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms , 2005, Expert Syst. Appl..

[15]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[16]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[17]  Peter J. Angeline,et al.  An evolutionary algorithm that constructs recurrent neural networks , 1994, IEEE Trans. Neural Networks.

[18]  Christof Koch,et al.  Computation and the single neuron , 1997, Nature.

[19]  Dan Givoli,et al.  Neural network time series forecasting of finite-element mesh adaptation , 2005, Neurocomputing.

[20]  Christof Koch,et al.  The role of single neurons in information processing , 2000, Nature Neuroscience.

[21]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[22]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[23]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[24]  Hisashi Shimodaira.  A method for selecting similar learning data in the prediction of time series using neural networks , 1996 .

[25]  L. Yi-Hui,et al.  Evolutionary neural network modeling for forecasting the field failure data of repairable systems , 2007, Expert Syst. Appl..