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In the paper, a novel method is proposed for global asymptotically robust stability of cellular neural networks with time-varying delay. New delay-dependent global asymptotically robust stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). Finally, numerical example is given to demonstrate the...
The paper presents evolving cellular automata-based scheduling production. The models have been implemented and tested, and the examples have been illustrated. The software of this model, allows us to analyze the process of construction schedule for many variants reflecting a variety of combinations other factors. The results indicate that the proposed algorithm is an efficient approach to solve FJSP.
A CNN is a dynamic nonlinear system with the local connectivity of cells and easy to be translated into a VLSI implementation. CNNs are very suitable for modeling the physical process of energy propagation since CNNs conserve the physical properties of a continuous structure. For the contour extraction, this paper proposes a method for establishing the evolution model of active contour by CNN technique...
In this paper, a model describing dynamics of Cohen-Grossberg-type bidirectional associative memory neural networks with neutral time-varying delays is investigated by using the continuation theorem of Mawhin's coincidence degree theory and the properties of an M-matrix. Without assuming the continuous differentiability of time-varying delays, some sufficient conditions on the existence of the periodic...
To investigate the issue of market share, people first perform market discrimination and then propose a model to explain, analyze, evaluate, and predict it. Traditional approach is through observation, questionnaire, and data analysis with statistic mechanisms. This is a static approach, which has its constraints and drawbacks in investigating problems in a dynamic environment. This study is to investigate...
Neural network has some characteristics like self-study, adaptation, better fault tolerance, which make it widely used in dealing with un-linear dynamic problem. An improved multi-user detection algorithm based on three-layer forward neural network was proposed, the transfer function was adopted, and the iteration equation was deduced. The computer simulations show that the proposed algorithm has...
The attraction of neural networks is that they are best suited to solving the problems that are the most difficult to solve by traditional computational methods. BP neural network is widely used in many fields. In this paper, analysed the control requirements of servo control system, combined control characteristics of neural network and PID based on mathematical model of permanent magnet linear synchronous...
Classification of underwater objects remains challenging and significant problem because of the complexity of underwater environments. In this paper,a probabilistic neural network (PNN) is used as a classifier to the automatic classification of underwater objects. Firstly, a process of multi-field feature extraction is employed to construct a feature vector.The multi-field feature extraction involves...
A new method for electric heating cable fault testing based on BP algorithm was proposed in this paper. The design process of a useful neural network was described and the principle chart of the detecting system was given. Experimental results showed that the method based on the neural network could be used to improve the effectiveness of locating the fault point of the electric heating cable.
A new scheme to estimate the moment of inertia in the motor drive system in very low speed is proposed. The simple speed estimation scheme, which is used in most servo systems for low-speed operation, is sensitivity to variations in machine parameters especially the moment of inertia. To estimate the motor inertia value, an extended Luenberger observer (ELO) is applied. The observer gain matrix can...
This paper investigates a new method for gas/liquid two-phase flow recognition by combining the features of Shannon's entropy and the recurrent neural networks. The information of the method that provided by cross-sectional measured resistance Information (CSMRI) is the measured data in horizontal pipe. The feature vector of Shannon's entropy that can express the essential information of gas/liquid...
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