Catálogo de publicaciones - libros

In order to explore the search mechanism of chaotic neural network(CNN), this paper first investigates the time evolutions of four chaotic noise models, namely Logistic map, Circle map, Henon map, and a Special Two-Dimension (2-D) Discrete Chaotic System. Second, based on the CNN proposed by Y. He, we obtain three alternate CNN through replacing the chaotic noise source (Logistic map) with Circle map, Henon map, and a Special 2-D Discrete Chaotic System. Third, We apply all of them to TSP with 4-city and TSP with 10-city, respectively. The time evolutions of energy functions and outputs of typical neurons for each model are obtained in terms of TSP with 4-city. The rate of global optimization(GM) for TSP with 10-city are shown in tables by changing chaotic noise scaling parameter and decreasing speed parameter . Finally, the features and effectiveness of four models are discussed and evaluated according to the simulation results. We confirm that the chaotic noise with the symmetry structure property of reverse bifurcation is necessary for chaotic neural network to search efficiently, and the performance of the CNN may depend on the nature of the chaotic noise.

The min-max modular neural network with Gaussian zero-crossing function (M-GZC) has locally tuned response characteristic and emergent incremental learning ability, but it suffers from quadratic complexity in storage space and response time. Redundant Sample pruning and redundant structure pruning can be considered to overcome these weaknesses. This paper aims at the latter; it analyzes the properties of receptive field in M-GZC network, and then proposes a strategy for pruning redundant modules. Experiments on both structure pruning and integrated with sample pruning are performed. The results show that our algorithm reduces both the size of the network and the response time notably while not changing the decision boundaries.

Humans use many different kinds of information from different sensory organs in motion tasks. It is important in human sensing to extract useful information and effectively use the multiple kinds of information. From the viewpoint of a computational theory, we approach the integration mechanism of human sensory and motor information. In this study, the modular structure of auto-encoder is introduced to extract the intrinsic properties about a recognized object that are contained commonly in multiple kind of information. After the learning, the relaxation method using the learned model can solve the transformation between the integrated kinds of information. This model was applied to the problem how a locomotive robot decides a leg’s height to climb over an obstacle from the visual information.

The radial basis function (RBF) neural networks have been widely used for approximation and learning due to its structural simplicity. However, there exist two difficulties in using traditional RBF networks: How to select the optimal number of intermediate layer nodes and centers of these nodes? This paper proposes a novel ART2/RBF hybrid neural networks to solve the two problems. Using the ART2 neural networks to select the optimal number of intermediate layer nodes and centers of these nodes at the same time and further get the RBF network model. Comparing with the traditional RBF networks, the ART2/RBF networks have the optimal number of intermediate layer nodes , optimal centers of these nodes and less error.

Urban traffic system is a complex system in a random way, it is necessary to optimize traffic control signals to cope with so many urban traffic problems. A multi-layer chaotic neural networks involving feedback (ML-CNN) was developed based on Hopfield networks and chaos theory, it was effectively used in dealing with the optimization of urban traffic signal timing. Also an energy function on the network and an equation on the average delay per vehicle for optimal computation were developed. Simulation research was carried out at the intersection in Jiangmen city in China, and which indicates that urban traffic signal timing’s optimization by using ML-CNN could reduce 25.1% of the average delay per vehicle at intersection by using the conventional timing methods. The ML-CNN could also be used in other fields.

The performance of Self Organizing Map (SOM) is always influenced by learn methods. The resultant quality of the topological formation of the SOM is also highly dependent onto the learning rate and the neighborhood function. In literature, there are plenty of studies to find a proper method to improve the quality of SOM. However, a new term “stiffness factor” has been proposed and was used in SOM training in this paper. The effect of the stiffness factor has also been tested with a real-world problem and got positive influence.

This paper proposed a novel self-adaptive wavelet network model for Regression Analysis. The structure of this network is distinguished from those of the present models. It has four layers. This model not only can overcome the structural redundancy which the present wavelet network cannot do, but also can solve the complicated problems respectively. Thus, generalization performance has been greatly improved; moreover, rapid learning can be realized. Some experiments on regression analysis are presented for illustration. Compared with the existing results, the model reaches a hundredfold improvement in speed and its generalization performance has been greatly improved.

This paper presents an evolutionary artificial neural network (EANN) to the prediction of the BF hot metal silicon content. The pareto differential evolution (PDE) algorithm is used to optimize the connection weights and the network’s architecture (number of hidden nodes) simultaneously to improve the prediction precision. The application results show that the prediction of hot metal silicon content is successful. Data, used in this paper, were collected from No.1 BF at Laiwu Iron and Steel Group Co..

This paper incorporates robustness into neural network modeling and proposes a novel two-phase robustness analysis approach for determining the optimal feedforward neural network (FNN) architecture in terms of Hellinger distance of probability density function (PDF) of error distribution. The proposed approach is illustrated with an example in this paper.

With the changing of the stimulus frequency, there are a lot of firing dynamics behaviors of interspike intervals (ISIs), such as quasi-periodic, bursting, period-chaotic, chaotic, periodic and the bifurcations of the chaotic attractor appear alternatively in Hodgkin-Huxley (H-H) neuron model. The chaotic behavior is realized over a wide range of frequency and is visualized by using ISIs, and many kinds of abrupt undergoing changes of the ISIs are observed in deferent frequency regions, such as boundary crisis, interior crisis and merging crisis displaying alternately along with the changes changes of external signal frequency, too. And there are many periodic windows and fractal structures in ISIs dynamics behaviors. The saddle node bifurcation resulted collapses of chaos to period-12 orbit in dynamics of ISIs is identified.