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A new approach for edge detection of noisy image by cellular neural network (CNN) is proposed in this paper. In order to get the reasonable template, the statistical characteristics of image are utilized, and Gibbs image model is employed to describe the stochastic dependence of an edge pixel on its neighbourhood. Based on stochastic edge image models, edge detection of noisy image is equivalent to seeking a minimum of a cost function. If the template of CNN is designed carefully, the energy function can be mapped properly to the cost function of stochastic edge image model, then CNN can be used for seeking the minimum of cost function. Genetic algorithm is efficient in the field of optimization, and we also utilized this algorithm to get the correct form of template. The results of computer simulation confirm that the new approach is very effective. Furthermore, this result also confirms that we can design template for many different questions based on statistical image model, and the area of application of CNN will be widened. Copyright © 2003 John Wiley & Sons, Ltd.

A new approach for multilevel image segmentation based on fuzzy cellular neural network(CNN) is proposed. Based on a novel fuzzy CNN, a new template is proposed for multilevel image segmentation. The result of computer simulation proves this approach is reasonable. The stability of the fuzzy neural network is also analyzed in this paper.

Cellular neural network is very suitable to solve communication problems because of its local connection. This paper summarizes a new approach for CNN's template design based on energy function, which is valid to such problems as equalizer and multi-user detector. This approach can combine the objective function of communication problems with CNN's energy function, and then obtain reasonable CNN's template. We also discuss the learning algorithm and hardware annealing joined with the approach. Simulation results prove that this method is valid to a class of communication problems

In this paper, on the basis of BP network, a new and more universal network, functional link BP (FLBP) network and its simplified network, functional weight BP (FWBP) network, are proposed. Then their algorithms, the selection of link functions and their relationships with other networks are discussed. Finally, by applying FLBP and FWBP network to a typical disease prediction example, simulation results indicate that, with appropriate selection of link functions, FLBP and FWBP network perform better than BP network in terms of speed, accuracy and robustness

In this paper, the simulation results of cellular neural network (CNN) equalizer in Bluetooth system are shown. Compared with traditional Bluetooth without CNN equalizer, the performance of improved system is more robust. There is 4 dB improvement in modified Bluetooth system when the channel is multi-path channel with additive white Gaussian noise. The structure of hardware CNN equalizer spectrum system is also shown

PCNN (pulse coupled neural network), a phenomenological model exhibiting synchronous pulse bursts, can be used in image processing efficiently by the specified algorithm corresponding to the specified application, but so far there has been no general design approach. This paper describes that the parallel pulse-spreading behavior of unit-linking PCNN for image processing, based on PCNN, is equal to the operation of mathematic morphology. Hereby we propose the general unit-linking PCNN design approach for binary image processing. In the meantime, in order to explain how to apply this new general design approach in the specified application, binary image denoising, binary image edge detection, binary image hole-filter based on unit-linking PCNN are analyzed respectively as examples.

A new hyper-chaos communication system based on cellular neural network (CNN) is proposed in this paper. Hyper-chaos is generated with fifteen-cell CNN, then it’s transferred to a digital sequence. The chaotic sequence is better than gold sequence when they are utilized in direct sequence spread spectrum system. Compared with the traditional gold sequence system, there is 3dB improvement in CNN chaotic sequence system when the channel is multi-path channel. Because of the complex dynamic behavior of hyper-chaos, security signal could be transferred through wireless channel. The structure of hardware CNN spread spectrum system is also shown, and the security of CNN communication is analyzed at last.

In this paper, a kind of relation between CNN (cellular neural network) and GIM (Gibbs image model) is noted. Based on this relation, a new approach for CNN's template design is proposed, this approach is valid to many questions that could be processed with GIM, such as segmentation, edge detection and restoration. We also discuss the learning algorithm and hardware annealing jointed with the new approach. Simulations of some examples are shown in order to validate effectiveness of new approach.

This paper introduces the DPCNN (Delay Pulse Coupled Neural Network) based on the PCNN and uses the DPCNN to find the shortest path. Cauflield and Kinser introduced the PCNN method to solve the maze[1] and although their method also can be used to find the shortest path, a large quantity of neurons are needed. However, the approach proposed in this paper needed very fewer neurons than proposed by Cauflield and Kinser. Meanwhile, due to the parallel pulse transmission characteristic of the DPCNN, our approach can find the shortest path quickly. The computational complexity of our approach is only related to the length of the shortest path, and independent to the weighted graph complexity and the number of existed paths in the graph.

A new spread spectrum communication system based on CNN is proposed in this paper. Chaos is generated with three cell CNN, then it’s transferred to a digital sequence. The chaotic sequence is better than gold sequence when they are utilized in direct sequence spread spectrum system. Compared with the traditional gold sequence system, there is 2dB improvement in CNN chaotic sequence system when the channel is additive white Gaussian noise channel, and there is more improvement in CNN chaotic sequence system when the channel is multi-path channel. The structure of hardware CNN spread spectrum system is also shown at last.

In this paper, a new kind of two-dimensional cellular automata (CA) is studied. Several algorithmic rules are discovered that can be used to design new cellular neural networks (CNNs) that can implement a shadow detector for character recognition. The Boolean expressions and learning algorithms are introduced in detail. The results of computer simulation confirm that this new approach is simpler and more effective than that given in the literature. Furthermore, these results also confirm that we can design a new cellular neural network with CA rules and find a novel method to design CNNs.

The PCNN (pulse coupled neural network), an artificial neural network based on biology, can be efficiently applied to image segmentation. The performance of image segmentation based on PCNN depends on suitable PCNN parameters. However, it is difficult to get suitable PCNN parameters for different kinds of images because different kinds of images have different suitable PCNN parameters. So far, no paper has described how to get the suitable PCNN parameters to efficiently segment images. In this paper, we put forward a new approach for image segmentation based on a unit-linking PCNN, by which we can use the same PCNN parameter to efficiently segment different kinds of images. Therefore, using this new approach can automatically and efficiently segment images without choosing different parameters for different kinds of images.

This paper proposes the application of Artificial Neural Networks (ANN) in forecasting miners' occupational disease incidence, examines the effect of BP network topology and parameters on training time, describes a neural network that uses Legendre polynomials as nonlinear weights and tries to find the most suitable network model to solve the problem via analyzing the forecast result. Through computer simulation, it clarifies that this model could forecast miners' pneumoconiosis incidence commendably.