The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

One of the main problems in chaotic time series prediction is that the underlying nonlinear dynamics is usually unknown. Using a nonlinear predictor to predict a chaotic time series usually puts a limit on the accuracy since the nonlinear predictor is basically an approximation of the unknown nonlinear mapping. In this paper, we propose using fusion of predictors as a method to improve the perf...

The study of the influence learning speed η on process a multilayer neural network was studied. program for written in Python. We determined at which best is observed. To analyze impact process, logistic function used, describes doubling frequency. It shown that error characterized by bifurcation processes lead to chaotic state η> 0.8. optimal value determined, determines appearance number l...

this paper presents a gaussian radial basis function neural network based on sliding mode control for trajectory tracking and vibration control of a flexible joint manipulator. to study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. the adaptation laws of designed controller are obtained based on sliding m...

Based on chaotic neural network, a multiple chaotic neural network algorithm combining two different chaotic dynamics sources in each neuron is proposed. With the effect of self-feedback connection and non-linear delay connection weight, the new algorithm can contain more powerful chaotic dynamics to search the solution domain globally in the beginning searching period. By analyzing the dynamic...

In our previous research, we confirmed that the chaotic switching noise generated by the cubic map gained a good performance for solving combinatorial optimization problems when the noise was injected to the Hopfield neural network. However, the reason of the good effect of chaotic switching noise has not been clarified completely. In this study, we investigate the solving ability of Hopfield n...

Electricity cannot be stored and needs huge amount of capital so producers and consumers pay special attention to predict electricity consumption. Besides, time-series data of the electricity market are chaotic and complicated. Nonlinear methods such as Neural Networks have shown better performance for predicting such kind of data. We also need to analyze other variables affecting electricity c...

Chaos is a revolutionary concept, which brings a novel strategy of science for researchers. In this paper, a chaotic neural network is proposed and the simulated annealing strategy also embedded to construct an annealed chaotic neural network (ACNN) and apply to the clustering problem. In addition to retain the characteristics of the conventional neural units, the ACNN displays a rich range of ...

By adding chaotic noise to each neuron of the discrete-time continuous-output Hopfield neural network (HNN) and gradually reducing the noise, a chaotic neural network is proposed so that it is initially chaotic but eventually convergent, and, thus, has richer and more flexible dynamics compared to the HNN. The proposed network is applied to the traveling salesman problem (TSP) and that results ...

A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is app...