Catálogo de publicaciones - libros

In the limestone/gypsum wet flue gas desulphurization (FGD) technology, the change of slurry pH value in absorber is a nonlinear and time-variation process with a large number of uncertainties, so it’s difficult to acquire satisfying mathematical model. In this paper, a novel multiple improved PIDNN ensemble model is proposed to establish the model of slurry pH value. In this model, the concepts of variable integral and partial differential are introduced in the design of hidden-layer of PIDNN, and the concept of output feedback is utilized to improve the ability of PIDNN for dynamic modeling, then multiple improved PIDNN are dynamic combined to get the system output. The results of simulation with field data of wet FGD indicate the validity of this modeling approach.

In this paper, we propose an approach for acoustic modeling using Hidden Markov Models (HMMs) in the Mercer kernel feature space. Acoustic modeling of subword units of speech involves classification of varying length sequences of speech parametric vectors belonging to confusable classes. Nonlinear transformation of the space of parametric vectors into a higher dimensional space using Mercer kernels is expected to lead to better separability of confusable classes. We study the performance of continuous density HMMs trained using the varying length sequences of feature vectors obtained from the kernel based transformation of parametric vectors. Effectiveness of the proposed approach to acoustic modeling is demonstrated for recognition of spoken letters in E-set of English alphabet, and for recognition of consonant-vowel type subword units in continuous speech of three Indian languages.

To decrease the influence of fuzzy and uncertain factors on the maintenance decision process of diesel engine, a fuzzy-neural-network-based maintenance decision model for diesel engine is presented in this paper. It can make the maintenance of diesel engine follow the prevention policy and take the technology and economy into account at the same time. In the presented model, the fuzzy logic and neural network is integrated based on the state detection technology of diesel engine. The maintenance decision process of diesel engine is analyzed in detail firstly. Then, the fuzzy neural network model of maintenance decision is established, including an entire network and two module sub-networks, where the improved T-S model is used to simply the structure of neural networks. Finally, an example is given to verify the effective feasibility of the proposed method. By training the network, the deterioration degree of the diesel engine and its parts can be obtained to make the right maintenance decision.

In vehicular traffic modelling, the effect of link capacity on travel times is generally specified through a delay function. In this paper, the Radial Basis Function Neural Network (RBFNN) method, integrated into a dynamic network loading process, is utilized to model delays at a highway node. The results of the model structure have then been compared to evaluate the relative performance of the integrated neural network method.

This paper presents an improved neural network model interpretating sonar readings to build occupancy grids of mobile robot. The proposed model interprets sensor readings in the context of their space neighbors and relevant successive history readings simultaneously. Consequently the presented method can greatly weaken the effects by multiple reflections or specular reflection. The output of the neural network is the probability vector of three possible status(empty, occupancy, uncertainty) for the cell. As for sensor readings integration, three probabilities of cell’s status are updated by the Bayesian update formula respectively, and the final status of cell is defined by Max-Min principle.Experiments performed in lab environment has shown occupancy map built by proposed approach is more consistent, accurate and robust than traditional method while it still could be conducted in real time.

This paper firstly provides a short introduction to least square support vector machine (LS-SVM), then provides sequential minimal optimization (SMO) based on Pruning Algorithms for LS-SVM, and uses LS-SVM to model nonlinear systems. Simulation experiments are performed and indicated that the proposed method provides satisfactory performance with excellent accuracy and generalization property and achieves superior performance to the conventional method based on common LS-SVM and neural networks.

The paper presents a pattern-oriented agent-based model to simulate the dynamics of a stock market. The model generates satisfactory market macro-level trend and volatility while the agents obey simple rules but follow the behaviors of the neighbors closely. Both the market and the agents are made to evolve in an environment where Darwin’s natural selection rules apply.

Estimating the non-flat function which comprises both the steep variations and the smooth variations is a hard problem. The existing kernel methods with a single common variance for all the regressors can not achieve satisfying results. In this paper, a novel multi-scale model is constructed to tackle the problem by orthogonal least squares regression (OLSR) with wavelet kernel. The scheme tunes the dilation and translation of each wavelet kernel regressor by incrementally minimizing the training mean square error using a guided random search algorithm. In order to prevent the possible over-fitting, a practical method to select termination threshold is used. The experimental results show that, for non-flat function estimation problem, OLSR outperforms traditional methods in terms of precision and sparseness. And OLSR with wavelet kernel has a faster convergence rate as compared to that with conventional Gaussian kernel.

Sensorlesstension control of multi-motor synchronous system with closed tension loop is required in many fields. How to identify the knowledge of instantaneous magnitude of tension is key. In this paper the tension identification is managed on the base of stator currents and its previous values with neural network. According to the fundamental state equations of multi-motor system for tension control, the novel method of tension identification using neural network is presented .A multi-layer feed-forward neural network (MFNN) is trained by Back Propagation Levenberger-Marquardt’s method. Simulation and experiment results show that the system with tension identification via a neural network has better performance, and it can be used in many application fields.

Operon represents a basic organizational unit in microbial genomes. Operon prediction is an important step to study genic transcriptional and regulatory mechanism in microbial genomes. This paper predicted operons in the Escherichia coli K12 genome using neural network based on four types of genomic log-likelihood data. First this method estimated the log-likelihood values for intergenic distances, COG gene functions, conserved gene pairs and phylogenetic profiles, and then used these information by a generalized regression neural network to discriminate pairs of genes within operons (WO pairs) or transcription unit borders (TUB pairs). We test the method on E. coli K12 and find that it can obtain average sensitivity, specificity and accuracy at 85.9%, 89.2% and 87.9% respectively, which indicates that the proposed method has a powerful capability for operon prediction.