An artificial neural network based on Izhikevich neuron model

Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

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Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

Neural Processing Letters ◽

10.1007/s11063-017-9686-3 ◽

2017 ◽

Vol 47 (3) ◽

pp. 1133-1147 ◽

Cited By ~ 4

Author(s):

Ozge Cagcag Yolcu ◽

Eren Bas ◽

Erol Egrioglu ◽

Ufuk Yolcu

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Neuron Model ◽

Autoregressive Coefficient ◽

Time Series Modelling ◽

Artificial Neural

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Fusion Control of Flexible Logic Control and Neural Network

Mathematical Problems in Engineering ◽

10.1155/2014/913549 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Lihua Fu ◽

Dan Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Neuron Model ◽

Stability Control ◽

Effective Control ◽

Universal Logic ◽

Pendulum System ◽

Logic Control ◽

Inverted Pendulum System ◽

Artificial Neural

Based on the basic physical meaning of errorEand error varietyEC, this paper analyzes the logical relationship between them and usesUniversal Combinatorial Operation ModelinUniversal Logicto describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model ofZero-level Universal Combinatorial OperationinUniversal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

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