Taiwan is a small island state surrounded by the oceans but with large population. With limited land space, it would be worthwhile considering how to stabilize the existing coast or to create stable artificial beaches. Under the onslaught of storm surge and large wave from typhoons, beach erosion would occur accompanying by formation of a submerged bar beyond the surf zone with the sand removed from the beach. After the storm, the bar material maybe transport back by the swell and predominant waves which helps recover the original beach, thus producing a beach profile in dynamic equilibrium. The main purpose of this research is to use the back-propagation neural network¡]BPNN¡^, which trains a sample model and creates a system for the estimation, prediction, decision making and verification of an anticipated event. By the BPNN, we can simulate the key characteristic parameters for the storm beach profile resulting from typhoon action. Source data for training and verification are taken from the experimental results of beach profile change observed in large-scale wave tank¡]LWT¡^conducted by Coastal Engineering Research Center¡]CERC¡^in the USA in the 1960s and that from the Central Research Institute of Electric Power Industry in Japan in the 1980s. Some of the data are used as training pairs and others for verification and prediction of the key parameters of berm erosion and bar formation. Through literature review and simulation on the related parameters for storm beach profile, methodology for the prediction of the beach profile and bar/berm characteristics can be established.

Unit commitment is one of the most important subjects with respect to the economical operation of power systems, which attempts to minimize the total thermal generating cost while satisfying all the necessary restrictive conditions. ¡@¡@This thesis proposes a short-term thermal generating unit commitment by genetic algorithm and back propagation network. Genetic algorithm is based on the optimization theory developed from natural evolution principles, and in the optimization process, seeks a set of solutions simultaneously rather than any single one by adopting stochastic movement rule from one solution to another, which prevents restriction to fractional minimal values. Neural networks method outperforms in speed and stability. This thesis uses back propagation network method to complete neural networks and sets the optimal unit combination derived from genetic algorithm as the target output. ¡@¡@Under fixed electrical systems, instant responsiveness can be calculated by neural networks. When the systematical architecture changes, genetic algorithm can be applied to re-evaluation of the optimal unit commitment, hoping to improve the pitfalls of traditional methods. ¡@¡@This thesis takes the power system of six units for example to conduct performance assessment. The results show that genetic algorithm provides solutions closer to the overall optimal solution than traditional methods in optimizing unit commitment. On the other hand, neural networks method can not only approximate the solution obtained by genetic algorithm but also process faster than any other methods.

Abstract The traditional fault inspection of the motorcar engine cannot detect the noise and sound signal resulted from the abnormalities of some mechanical parts. For instance, the cylinder misfires; the looseness of the fan belt is irregular; the valve clearance is out of order¡K. and so on. When the fault message cannot be delivered by the ECU of the computer, the skilled senior engineers are required at this moment to make the experiential judgments. In the present society, due to the development of information, the computer technology makes progress by leaps and bounds. If we can make use of the monitoring method by the Acoustic signal instrument, build up a set of complete and efficient fault diagnosis system through the computer software and apply speedy and accurate way to assist the repairmen in relocating the causes for such faults, the accuracy of inspection can be greatly enhanced with a huge help in the preventive maintenance work. In that case, the fault conditions of the engine can be validated precisely and effectively, so the overhaul efficiency of the engine can be upgraded to a large extent. In this article, the procedures of sound signal recording will be brought forward by linking the digital camera with such a recording equipment as the high-precision microphone to make records of the fault sounds made when the engine runs. It uses the frequency analyzer to conduct the sampling and combine the computer software to further process and analyze the same. Finally the character parameters will be obtained. By applying the mathematical exercise of ¡§Back-Propagation Neural Network¡¨ to undertake the training and detection of the sounds for the purpose of identifying the kinds of the faults. It replaces the errors caused from the experiential judgments made by the expert senior engineers. In terms of the training and maintenance ability of the newly recruited technical repairmen, their capability for exact and reasonable recognition of the fault types is substantially promoted. Keywords¡GAcoustic Signal¡ABack Propagation Neural Network

Conventional time series analysis depends heavily on the twin assumptions of linearity and stationarity. However; there are certain cases where sampled data tend to violate the assumptions. In this paper, we use neural networks technology to explore the situation when the assumptions of linearity and stationarity are failed. At the end of the paper, we discuss an illustrative example about the annual expenditures of government and science-education-culture of R.O.C.

Wing-Chung Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 81-85). / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Multiresolution Signal Decomposition --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Laplacian Pyramid --- p.6 / Chapter 2.2.1 --- Gaussian Pyramid Generation --- p.7 / Chapter 2.2.2 --- Laplacian Pyramid Generation --- p.7 / Chapter 2.2.3 --- Decoding --- p.8 / Chapter 2.2.4 --- Limitation --- p.9 / Chapter 2.3 --- Multiresolution Transform --- p.9 / Chapter 2.3.1 --- Multiresolution Approximation of L2(R) --- p.9 / Chapter 2.3.2 --- Implementation of a Multiresolution Transform --- p.12 / Chapter 2.3.3 --- Orthogonal Wavelet Representation --- p.16 / Chapter 2.3.4 --- Implementation of an Orthogonal Wavelet Representation --- p.18 / Chapter 2.3.5 --- Signal Reconstruction --- p.21 / Chapter 3 --- Multiresolution Learning Method --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Input Vector Representation --- p.24 / Chapter 3.2.1 --- Representation at the resolution 1 --- p.24 / Chapter 3.2.2 --- Representation at the resolution 2j --- p.25 / Chapter 3.2.3 --- Border Problem --- p.26 / Chapter 3.3 --- Back-Propagation Network Architecture --- p.26 / Chapter 3.4 --- Training Procedure Strategy --- p.27 / Chapter 3.4.1 --- Sum Squared Error (SSE) --- p.28 / Chapter 3.4.2 --- Intermediate Stopping Criteria --- p.30 / Chapter 3.5 --- Connection Weight Transformation --- p.31 / Chapter 3.5.1 --- Weights between the Input and Hidden Layers --- p.31 / Chapter 3.5.2 --- Weights between the Hidden and Output Layers --- p.33 / Chapter 4 --- Simulations --- p.36 / Chapter 4.1 --- Introduction --- p.36 / Chapter 4.2 --- Choices of the Impulse Response h(n) --- p.36 / Chapter 4.3 --- XOR Problem --- p.39 / Chapter 4.3.1 --- Setting of Experiments --- p.39 / Chapter 4.3.2 --- Experimental Results --- p.41 / Chapter 4.4 --- Numeric Recognition Problem --- p.50 / Chapter 4.4.1 --- Setting of Experiments --- p.50 / Chapter 4.4.2 --- Experimental Results --- p.52 / Chapter 4.5 --- Discussions --- p.72 / Chapter 5 --- Conclusions --- p.75 / Chapter A --- Proof of Equation (4.9) --- p.77 / Chapter B --- Proof of Equation (4.11) --- p.79 / Bibliography --- p.81

In this research, a single PAN-TILT image servo system has been developed with real-time face tracing technology. First, the target face is detected, and then the target template is kept at the image center with the integration of optical flow algorithm and control theory. In motion control, back-propagation neural network is taken to predict and estimate the target position. Experiments are made to analyze the performance of the video servo control system.

ABSTRACT In this thesis, some aspects of the non-fixed neural network for power load forecasting are discussed. Unlike traditional fixed neural network technique, the structure of neural network is non-fixed during its training and testing phases. Based on the characteristic of the desired forecasting day, the number of input node utilized is changeable. The modified learning algorithms, including fuzzy back-propagation learning algorithm and stochastic back-propagation learning algorithm, will be used in the load forecasters we developed. For precise input selection of the neural network model, the analysis of mutual relationship between load and temperature and gray relational analysis between desired forecasting load and the related previous load are studied. Two types of load forecasting, i.e., peak load forecasting and hourly load forecasting, are investigated. Short term (one-to-several-day-ahead) load forecasting is considered in this research. Hourly loads and relevant temperature data from 1992 to 1998 provided by Taipower Utility and the Central Weather Bureau is implemented for this research. For demonstrating the feasibility and superiority of the forecasters we develop, several forecasting models, including fixed neural network with constant learning rate and momentum, recursive time series model, and artificial neural network short term load forecaster (ANNSTLF) proposed by [Kho.2], are also performed for a comparison. From the results of the simulation, better performances could be obtained by the methods we proposed. Not only the over-training phenomenon is obviously reduced, the forecasting accuracy and the learning speed of the neural model are also effectively improved.

This objective of thesis is to derive the adaptive load shedding by artificial neural network (ANN) so that the amount of load shedding can be minimized. An actual industrial customer and Taipower system are selected for computer simulation to fit the ANN model. The mathematical models of generation, exciters, governors and loads are used in the simulator program. The back propagation neural method is considered for the neural network training of load shedding.To create the training data set for ANN models, the transient stability analysis is performed to fit the load shedding under different operation and fault condition. The back propagation method and L-M learning process are then used to fit the minimum load shedding without causing system stability problem. To verify the effectiveness of the proposed methodology for adaptive load shedding, three fault contingencies for both the industrial cogeneration system and Taipower system have been simulated. By compare to the conventional load shedding, it is found that the amount of load shedding can be minimized and adjusted according to the real time operation conditions of power systems.

In the study of sound identification, land animals such as birds and bats have been well investigated, and so are their habitats. On the other hand, sound making creatures in the ocean are much less researched. In this research, the stress calls of three Holocentridaes, Neoniphon sammara, Myripristis murdjan, and Sargocentron spinosissimum, who are commonly found in coral reefs, were recorded in water tank for analysis of sound characteristics. The averaged characteristic parameters of single pulse among three is around 410 Hz for the peak frequency, 100 Hz for the bandwidth, 0.07 dB/Hz for the slope, and duration of 0.05 s. As for the impulse train, averaged peak frequency is 415 Hz, 55 Hz for the bandwidth, 0.07 dB/Hz for the slope, and duration of 0.5 s. These parameters were first checked by the Kolmogorov-Smirnov Test to identify if each parameter follows normal distribution; the slopes of ascending and descending frequency and the total duration are not in normal distribution. The three parameters were later transferred so as to concentrate variances. Next, analysis of variance was applied on all characteristics to extract the significant parameters (including non transferred and transferred data), which were then tested by Stepwise Discriminat and Back-propagation Network. The identification rate of for single pulse with and without data transfer is 63% and 82% while pulse train is 57% and 73%. Both identification rates were raised up approximately 20% due to the data transfer. Both methods provide an reliable tool for marine sound identification, and the whole process of the study may be applied to another biological identification.

Šiame darbe detaliai išanalizuotas klaidos skleidimo atgal algoritmas, atlikti tyrimai. Išsamiai analizuota neuroninių tinklų teorija. Algoritmui taikyti ir analizuoti sistemoje Visual Studio Web Developer 2008 sukurta programa su įvairiais tyrimo metodais, padedančiais ištirti algoritmo daromą klaidą. Taip pat naudotasi Matlab 7.1 sistemos įrankiais neuroniniams tinklams apmokyti. Tyrimo metu analizuotas daugiasluoksnis dirbtinis neuroninis tinklas su vienu paslėptu sluoksniu. Tyrimams naudoti gėlių irisų ir oro taršos duomenys. Atlikti gautų rezultatų palyginimai. / The present work provides an in-depth analysis of the error back-propagation algorithm, as well as information on the investigation carried out. A neural network theory has been analysed in detail. For the application and analysis of the algorithm in the system Visual Studio Web Developer 2008, a program has been developed with various investigation methods, which help to research into the error of the algorithm. For training neural networks, Matlab 7.1 tools have been used. In the course of the investigation, a multilayer artificial neural network with one hidden layer has been analysed. For the purpose of the investigation, data on irises (plants) and air pollution have been used. Comparisons of the results obtained have been made.