Catálogo de publicaciones - libros

This paper presents an adaptive threshold neural-network scheme for Rotorcraft Unmanned Aerial Vehicle (RUAV) sensor failure diagnosis. The approach based on adaptive threshold has the advantages of better detection and identification ability compared with traditional neural-network-based scheme. In this paper, the proposed scheme is demonstrated using the model of a RUAV and the results show that the adaptive threshold neural-network method is an effective tool for sensor fault detection of a RUAV.

Kernel principal component analysis (KPCA) is widely used for fault detection. In this paper, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of the improvements for fault detection performance in terms of high fault detection rate.

The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. The paper describes a systematic approach for detecting superfluous data. It is considered as a ”white box” rather than a ”black box” like in the case of neural network. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. To deal with the uncertainty and deferent structures of the system, rough sets and fuzzy sets are introduced. The reduction algorithm based on uncertainty rough sets is improved. The rule reliability is deduced using fuzzy sets and probability. The simulation result of a power distribution system shows the effec-tiveness and usefulness of the approach.

A design method of associative memory model with expecting fault-tolerant field is proposed.The benefit of this method is to make the designed associative memory model memory sample fault-tolerant field which implements the hoped situation. For any different P samples in dimensional binary information space  = [1, − 1] and any the compartmentalization ,,..., of , an associative memory model with expecting fault-tolerant field ,,..., can be designed by the method. The method better solves the difficult synthesis problems of associative memory models.

This manuscript introduces a fault diagnosis system for a turbine-governor system that is an important control system in a nuclear power plant. Because the turbine governor system is operated by high oil pressure, it is very difficult to maintain the operating condition properly. The turbine valves in the turbine governor system supply an oil pressure for operation. Using the pressure change data of the turbine valves, the condition of the turbine governor control system is evaluated. This study uses fuzzy logic and neural networks to evaluate the performance of the turbine governor. The pressure data of the turbine governor and stop valves is used in the turbine governor diagnosis algorithms. The features of the pressure signals are defined to be applied in the fuzzy diagnosis system. And Fourier transformed signals of the pressure signals are used in the neural network models for diagnosis. The diagnosis results both by fuzzy logic and neural networks are compared to evaluated the performance of the designed system.

A novel method for the multiuser detection in CDMA communication systems based on a stochastic cellular neural network (SCNN) is proposed in this paper. The cellular neural network (CNN) can be used in multiuser detection, but it may get stuck in a local minimum resulting in a bad steady state. The annealing CNN detector has been proposed to avoid local minima; however, the near-far effect resistant performance of it is poor. So, the SCNN detector is proposed here through adding a stochastic term in a CNN. The performance of the proposed SCNN detector is evaluated via computer simulations and compared to that of the conventional detector, the stochastic Hopfield network detector, and the Annealing CNN detector. It is shown that the SCNN detector can avoid local minima and has a much better performance in reducing the near-far effect than these detectors, as well as a superior performance in bit-error rate.

This paper develops a new blind channel identification method in frequency domain. Oversampled signal has the property of spectral redundancy in frequency domain which is corresponding to the cyclostationarity property in time domain. This method exploits the cyclostationarity of oversampled signals to identify possibly non-minimum phase FIR channels. Unlike many existing methods, this method doesn’t need EVD or SVD of correlation matrix. Several polynomials are constructed and zeros of channels are identified through seeking for common zeros of those polynomials. It is in the similar spirit of Tong’s frequency approach, but this new algorithm is much simpler and computationally more efficient. A sufficient and necessary condition for channel identification is also provided in this paper. This condition is quite similar to Tong’s time domain theory but it is derived from a novel point of view.

The recently introduced square contour algorithm (SCA) combines the benefits of the generalized Sato algorithm (GSA) and the constant modulus algorithm (CMA). It implicitly updates phase and is less likely to converge to incorrect solutions. But the SCA has relatively large residual error after the algorithm reaches its steady state for high-order constellations. A new blind equalization algorithm is proposed based on concurrent square contour algorithm (SCA) and soft decision-directed (SDD) adaptation. Like the SCA, the proposed concurrent SCA and SDD algorithm includes phase recovery and offers good convergence characteristics. Simulation results demonstrate that the proposed SCA+SDD algorithm offers practical alternatives to blind equalization of high-order QAM channels and provides significant equalization improvement over the CMA, GSA and SCA.

Scarcity of the spectrum resource and mobility of users make Quality-of-Service (QoS) provision a critical issue in wireless multimedia networks. This paper uses neural network as call admission controller to perform call admission decision. A performance measurement is formed as a weighted linear function of new call and handoff call blocking probabilities of each service class. Simulation compares the neural network with complete sharing policy. Simulation results show that neural network has a better performance in terms of average blocking criterion.

Using the neural network model for oriented principal component analysis (OPCA), we propose a solution to the data least squares (DLS) problem, in which the error is assumed to lie in the data matrix only. In this paper, We applied this neural network model to channel equalization. Simulations show that DLS outperforms ordinary least square in channel equalization problems.