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AVHRR IMAGE SEGMENTATION USING MODIFIED BACKPROPAGATION algorithm

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1994年第4期E77D卷 490-497页

作者： CHEN, T TAKAGI, M Univ of Tokyo Tokyo Japan

Analysis of satellite images requires classification of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algorithm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

关键词： FEEDFORWARD NEURAL NETWORK learning algorithm NEURAL NETWORK APPLICATION IMAGE SEGMENTATION VISUAL INFORMATION PROCESSING

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Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

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SENSORS 2021年第21期21卷 7419-7419页

作者： Wang, Jing-he Tavoosi, Jafar Mohammadzadeh, Ardashir Mobayen, Saleh Asad, Jihad H. Assawinchaichote, Wudhichai Vu, Mai The Skruch, Pawel Huaqiao Univ Sch Econ & Finance Quanzhou 362021 Peoples R China Ilam Univ Dept Elect Engn Ilam 69315516 Iran Univ Bonab Elect Engn Dept Bonab *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Palestine Tech Univ Fac Sci Appl Dept Phys Tulkarm POB 7 Tulkarm Palestine King Mongkuts Univ Technol Thonburi Dept Elect & Telecommunicat Engn Fac Engn Bangkok 10140 Thailand Sejong Univ Sch Intelligent Mech Engn Seoul 05006 South Korea AGH Univ Sci & Technol Dept Automat Control & Robot PL-30059 Krakow Poland

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

关键词： learning algorithm fault detection type-3 fuzzy logic non-Gaussian noise correntropy Kalman filter

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Coordinated control scheme for provision of frequency regulation service by virtual power plants

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APPLIED ENERGY 2022年 325卷

作者： Oshnoei, Arman Kheradmandi, Morteza Blaabjerg, Frede Hatziargyriou, Nikos D. Muyeen, S. M. Anvari-Moghaddam, Amjad Shahid Beheshti Univ Fac Elect Engn Tehran Iran Aalborg Univ Dept Energy DK-9220 Aalborg Denmark Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Qatar Univ Dept Elect Engn Doha Qatar

This paper proposes a coordinated control strategy for a Virtual Power Plant (VPP) contribution to load frequency control. The considered VPP comprises distributed Battery Energy Storage Systems (BESSs) and Heat Pump Water Heaters (HPWHs). The frequency regulation signal is distributed between thermal generator and the VPP based on distribution coefficients which are calculated through conducting a multi-objective optimization problem. The optimization framework incorporates the dynamic regulation performance as well as the total regulation cost. A fuzzy strategy is adopted to obtain the final solution according to user-defined conditions. The regulation signal of VPP is dispatched based on the speed and the available power capacity of VPP components. The performance of the proposed coordination scheme is compared to the scheme without coordination and that with no involvement of VPP in frequency regulation. The regulation performance is also evaluated for varying time delays expected in the communication channels. An approach based on brain emotional learning is developed to coordinate the VPP and conventional generation unit to avoid large frequency deviations caused by the communication delays. Case studies are conducted on a multi-area power system in MATLAB/Simulink environment, and the results are verified by the OPAL-RT real-time simulator.

关键词： Battery Energy Storage System (BESS) learning algorithm Load frequency control (LFC) Heat pump water heater (HPWH) Virtual power plant (VPP)

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A novel fractional-order type-2 fuzzy control method for online frequency regulation in ac microgrid

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期90卷 103483-000页

作者： Mohammadzadeh, Ardashir Kayacan, Erkan Univ Bonab Elect Engn Dept Bonab Iran Univ Queensland Sch Mech & Min Engn Brisbane Qld 4072 Australia

In this paper, a novel adaptive fractional-order fuzzy control method is developed for frequency control in an ac microgrid (MG). A sequential general type-2 fuzzy system based on the radial basis neural network is presented for online modeling of the frequency response of the MG. Then, the parameters of the type-2 fuzzy controller based on the online estimated model are online tuned, such that the frequency deviation is minimized. The consequent parameters, i.e., centers of membership functions (MFs), the values of alpha-cuts, and the type-reduction parameters are optimized based on the proposed algorithm, which is inspired from the particle swarm optimization and artificial bee colony algorithm (PSO-ABC). The simulation results and comparison with other methods show that the proposed control scheme is effective, and results in a good and robust performance in the presence of variation of solar radiation, wind speed, load disturbance, and time-varying dynamics of the other units of MG. Moreover, the effectiveness of the proposed fuzzy system and the learning algorithm are examined by using white noise as the control input, and it is shown that the proposed identification scheme results in good performance even in the noisy environment.

关键词： General type-2 fuzzy neural network AC microgrid Frequency regulation learning algorithm

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Continuous wavelet process neural network and its application

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NEURAL NETWORK WORLD 2007年第5期17卷 483-495页

作者： Zhong Shisheng Li Yang Ding Gang Lin Lin Harbin Inst Technol Sch Mech & Elect Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, a continuous wavelet process neural network (CWPNN) model is proposed based on the wavelet theory and process neural network model. The network offers good compromise between robust implementations resulting from the redundancy characteristic of non-orthogonal wavelets, and efficient functional representations that build on the time-frequency localization property of wavelets. Moreover, the network can deal with continuous input signals directly. The corresponding learning algorithm is given and the network is used to solve the problems of aeroengine condition monitoring. The simulation test results indicate that the CWPNN has a faster convergence speed and higher accuracy than the same scale process neural network (PNN) and BP neural network. This provided an effective way for the problems of aeroengine condition monitoring.

关键词： process neuron continuous wavelet process neural network (CWPNN) learning algorithm aeroengine condition monitoring

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GAM: A general auto-associative memory model

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2002年第7期E85D卷 1153-1164页

作者： Shi, H Zhao, YX Zhuang, XH Ren, FJ Univ Missouri Dept Comp Sci & Comp Engn Columbia MO 65211 USA Univ Tokushima Fac Engn Tokushima 7708506 Japan

This paper attempts to establish a theory for a general auto-associative memory model. We start by defining a new concept called supporting function to replace the concept of energy function. As known, the energy function relies on the assumption of symmetric interconnection weights, which is used in the conventional Hopfield auto-associative memory, but not evidenced in any biological memories. We then formulate the information retrieving process as a dynamic system by making use of the supporting function and derive the attraction or asymptotic stability condition and the condition for convergence of an arbitrary state to a desired state. The latter represents a key condition for associative memory to have a capability of learning from variant samples. Finally, we develop an algorithm to learn the asymptotic stability condition and an algorithm to train the system to recover desired states from their variant samples. The latter called sample learning algorithm is the first of its kind ever been discovered for associative memories. Both recalling and learning processes are of finite convergence, a must-have feature for associative memories by analogy to normal human memory. The effectiveness of the recalling and learning algorithms is experimentally demonstrated.

关键词： bidirectional associative memory support function learning algorithm recalling algorithm

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A Neural Network-Based Adaptive Pole Placement Controller for Nonlinear Systems

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IFAC Proceedings Volumes 1997年第6期30卷 423-428页

作者： Wang Fuli Li Mingzbong Yang Yinghua Department of Automatic Control Northeastern University Shengyang Liaoning P.R. China

A new adaptive pole placement controller for nonlinear systems using a modified neural network is presented. The modified neural network is composed of two parts: one is a linear neural network (LNN), and the other is a multilayer feedforward neural network C:MFNN). Then a fast learning algorithm is proposed for training the network. The adaptive control design is based on the LNN and MFNN. Simulation results reveal that the new adaptive pole placement controller can effectively control a class of nonlinear systems. Un neuf adaptable pôle assignation contrôleur pour non-linéaire systèms lequels fait usage de modifiable nerveux réseau est présenté ici. Le modifiable nerveux réseau est compose de deux parts: un linéaire nerveux réseau (LNR) et un non-linéaire mutilassise nerveux réseau (NMNR). Une méthode pour apprendre algorithme rapidement est offert aussi. Le adaptable contrôl dessein est basé sur LNR et NMNR. Les simulation résultats révélent que le neuf adaptable pôle assignation contrôleur peut contrôller effectivement un type des non-linélaire systèmes.

关键词： pole placement adaptive control nonlinear systems neural networks learning algorithm

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TWO DIMENSIONAL FEEDRATE CONTROL FOR HIGH PERFORMANCE CNC MACHINE TOOLS

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IFAC Proceedings Volumes 2005年第1期38卷 311-316页

作者： Seung Soo Lee Jung Hwan Cho Gi Joon Jeon School of Electrical Engineering and Computer Science Kyungpook National University Taegu 702-701 South Korea

This paper proposes a new feedrate control technique of CNC that can achieve high machining accuracy and high productivity. The proposed adaptive neuro-controller adjusts both components of the feedrate and makes an improved command of contour geometry. This control architecture consists of a neural network identifier (NNI) and an iterative learning algorithm with inversion of the NNI. The NNI is an identifier for the non-linear characteristics of CNC and composed of two outputs that are identified with individual axis dynamics of the contour error. The iterative learning algorithm is exploited to derive an optimal feedrate control law by minimizing a performance index that is a measurement of the contour error and the machining time.

关键词： CNC neural network learning algorithm performance index geometry

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The Finite Element Neural Network Application to Plant Factory

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IFAC Proceedings Volumes 1993年第2期26卷 1155-1158页

作者： H. Murase S. Koyama Department of Agricultural Engineering University of Osaka Prefecture 1-1 Gakuen Sakai Osaka 593 Japan

A spatial neural network (FENN) has been developed. The FENN is structured in a physical space unlike other conventional artificial neural networks of which structure represent only mathematical connections of neural elements. The FENN structure is formed by the finite clement used in the finite element method. The governing differential equation (Poisson's equation) was utilized for neural information processing. Kalman filter was used as a learning algorithm of the FENN. In this paper, a practical aspect of the FENN in the field of plant factory is discussed in relation to the sensor fusion technique for environmental control

关键词： Neural nets Finite element method learning algorithm Kalman filters Sensor fusion

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Robustness analysis of power system under sequential attacks with incomplete information

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期232卷

作者： Tu, Haicheng Gu, Fengqiang Zhang, Xi Xia, Yongxiang Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Beijing Kedong Elect Power Control Syst Co Ltd Beijing 100192 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Modern power system demonstrates good performance to withstand a single failure. However, the recent progress has shown that power system is increasingly threatened by the sequential attacks. In this paper, we aim at evaluating the robustness of power system under the sequential attacks with incomplete information, taking into account both the electrical properties and the cascading failure process. In light of this, we first formulate the sequential attacks as a partial observable Markov decision process, and use Deep Q-network algorithm to identify the optimal attack sequence. The influences of network structures, attacks methods and protection measures are demonstrated in complex networks and IEEE 118-Bus system. Experimental results show that our proposed algorithm can effectively identify the optimal attack sequence under different attack methods and protection measures. Moreover, a larger redundant parameter and homogeneous network can improve the robustness of power system. Our findings can provide practical insights for building a robust power system.

关键词： Robustness analysis Sequential attacks Power system Incomplete information learning algorithm

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