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A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

ENERGIES 2011年第3期4卷 488-503页

作者： Amjady, Nima Keynia, Farshid Semnan Univ Dept Elect Engn Semnan Iran

Short-term load forecast (STLF) is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

关键词： STLF neural network learning algorithm harmony search

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Package flow models by neural network representation for understanding the dynamic behavior of nuclear reactor systems

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JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 1996年第9期33卷 675-685页

作者： Matsuoka, H Ishiguro, M SCI & TECHNOL AGCY TOKYO 100 JAPAN IBARAKI UNIV FAC ENGN HITACHI IBARAKI 316 JAPAN

''Package Flow Model'' (PFM) is a simple simulation model for intuitive understanding of various types of system dynamics. In the previous papers, the PFM was proposed and its application to the dynamic analysis of nuclear reactor systems was presented. In the present paper, the same model and same application are considered but a new representation method of the PFMs by a neural network is introduced, so that the dynamic simulation of the reactor subsystem can be performed through the calculation of corresponding neural network. Furthermore, the quasi optimum parameter values of each PFM are easily obtained by applying appropriate learning algorithm to get weight-values of the neural network. Some case studies show that the learning process and the obtained optimum values can give us new useful information on approximate understanding of the dynamic behavior of actual processes in the system.

关键词： reactor system simulation package flow model reactor dynamics reactor kinetics artificial neuron neural network learning algorithm automatic optimization

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Application of fuzzy cognitive maps for cotton yield management in precision farming

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第10期36卷 12399-12413页

作者： Papageorgiou, Elpiniki I. Markinos, Athanasios Gemptos, Theofanis Technol Educ Inst TEI Lamia Dept Informat & Comp Technol Lamia Athens 35100 Lamia Greece Univ Thessaly Lab Farm Mechanizat Dept Agr Crop Prod & Rural Environm Thessaly Greece

The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps (FCMs) for characterizing cotton yield behavior is presented in this research work. FCM is a modelling approach based on exploiting knowledge and experience. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps to handle experts' knowledge and on the unsupervised learning algorithm for FCMs to assess measurement data and update initial knowledge. The advent of precision farming generates data which, because of their type and complexity. are not efficiently analyzed by traditional methods. The FCM technique has been proved from the literature efficient and flexible to handle experts' knowledge and through the appropriate learning algorithms can update the initial knowledge. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P. Mg, N, Ca. Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton field. The proposed method was evaluated for 360 cases measured for three subsequent years (2001, 2003 and 2006) in a 5 ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of 75.55%, 68.86% and 71.32%, respectively for the years referred, in estimating/predicting the yield between two possible categories ("low" and "high"). The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Fuzzy cognitive maps Modeling Expert knowledge learning algorithm Unsupervised learning Decision making Cotton Yield Soil

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algorithm for automatic generation of fuzzy rules applied to power system controllers

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1998年第2期145卷 161-167页

作者： Luciano, AM Lauria, D Napoli, E Univ Naples Dept Elect I-80125 Naples Italy Univ Cassino Dept Ind I-03043 Cassino Italy

A new fuzzy algorithm for the identification of the input-output characteristic of a general system is applied to a power system controller. In particular the method for the generation of fuzzy rules is applied to a fuzzy logic variable-structure FACTS controller for electrical power system stability improvement. The fuzzy algorithm allows one to obtain a simple analytical form for the input-output characteristic of the identified system. The of identification is regarded as a nonlinear optimisation problem solved by means of the quasi-Newton optimisation method. The effectiveness of the proposed approach is verified through numerical simulations.

关键词： FACTS controller fuzzy logic controller quasi-Newton technique learning algorithm VSC controller

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A Stable Online algorithm for Energy-Efficient Multiuser Scheduling

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2010年第10期9卷 1391-1406页

作者： Salodkar, Nitin Karandikar, Abhay Borkar, Vivek S. ITPL Gen Motors Indian Sci Lab Bengaluru India Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India Tata Inst Fundamental Res Technol & Comp Sci Bombay 400005 Maharashtra India

In this paper, we consider the problem of energy-efficient uplink scheduling with delay constraint for a multiuser wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard constraint on another (average delay). We do not assume the arrival and channel statistics to be known. To handle state-space explosion and informational constraints, we split the problem into individual CMDPs for the users, coupled through their Lagrange multipliers;and a user selection problem at the base station. To address the issue of unknown channel and arrival statistics, we propose a reinforcement learning algorithm. The users use this learning algorithm to determine the rate at which they wish to transmit in a slot and communicate this to the base station. The base station then schedules the user with the highest rate in a slot. We analyze convergence, stability, and optimality properties of the algorithm. We also demonstrate the efficacy of the algorithm through simulations within IEEE 802.16 system.

关键词： Multiuser fading channel constrained Markov decision process energy-efficient scheduling learning algorithm

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A novel modification to backpropagation sample selection strategy

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 1997年第1-2期389卷 233-236页

作者： Redei, L Wallinga, H UNIV TWENTE DEPT ELECT ENGNMESA RES INSTNL-7500 AE ENSCHEDENETHERLANDS RES INST MAT SCI BUDAPESTHUNGARY

Random sample selection method in backpropagation results in convergence on the error (root of mean squared error, RMSE) surface. These problems, which are caused by the extreme (worst-case) errors, can be solved by a different sample selection strategy. A sample selection strategy has been proposed, which provides lower maximal errors and a higher confidence level on the expense of slightly increased RMSE. Applications are presented in the held of spectroscopic ellipsometry (SE), a sensitive, non-destructive but indirect analytical technique. Demonstrative example shows feature common to simulated annealing in the sense of escaping local minima.

关键词： neural network backpropagation learning algorithm ellipsometry

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Sleeping experts and bandits approach to constrained Markov decision processes

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AUTOMATICA 2016年 63卷 182-186页

作者： Chang, Hyeong Soo Sogang Univ Dept Comp Sci & Engn Seoul South Korea

This communique presents simple simulation-based algorithms for obtaining an approximately optimal policy in a given finite set in large finite constrained Markov decision processes. The algorithms are adapted from playing strategies for "sleeping experts and bandits" problem and their computational complexities are independent of state and action space sizes if the given policy set is relatively small. We establish convergence of their expected performances to the value of an optimal policy and convergence rates, and also almost-sure convergence to an optimal policy with an exponential rate for the algorithm adapted within the context of sleeping experts. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Markov decision processes Sleeping experts and bandits learning algorithm Constrained optimization

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Flexible ICA solution for nonlinear blind source separation problem

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ELECTRONICS LETTERS 2003年第22期39卷 1616-1617页

作者： Vigliano, D Uncini, A Univ Roma La Sapienza Dept Info Com I-00184 Rome Italy

Presented is a new architecture and a new learning algorithm that are exploited to resolve the blind source separation problem under stricter constraints than those considered to date. The mixing model that is assumed is an evolution of the well-known post-nonlinear (PNL) one: the PNL mixing block is followed by a convolutive mixing channel. The flexibility of the algorithm originates from the spline-SG neurons performing an on-line estimation of the score functions.

关键词： blind source separation signal sampling independent component analysis nonlinear distortion speech processing convolution splines (mathematics) intersymbolinterference neural nets flexible ICA solution nonlinear blind source separation problem learning algorithm mixing model post-nonlinear mixing block convolutive mixing channel algorithmflexibility spline-SG neurons online estimation score functions independent component analysis adaptive neural network nonlinear hidden mixing model signal recover nonlinear distortingfunctions female voice male voice ISI coefficient

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Forging a Close Relationship with Multimedia Communities

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IEEE MULTIMEDIA 2014年第4期21卷 14-15页

作者： Zeng, Wenjun Liu, Zicheng Steinbach, Eckehard Univ Missouri Columbia MO 65211 USA Microsoft Res Asia Beijing Peoples R China Microsoft Res Redmond WA USA Tech Univ Munich D-80290 Munich Germany

In May 2014, the authors of the top 26 papers from the IEEE International Conference on Multimedia & Expo (ICME) 2014 were invited to submit extended versions of their papers to this fast track special issue. After a rigorous peer-review process, eight of those submissions were accepted for this special issue, now titled "Hot Topics in Multimedia Research." This is just the beginning of a close collaboration between MM and major multimedia conferences.

关键词： Distributed Video Coding Multimedia Multimedia Research ICME Visual Analysis Object Tracking Image Quality Assessment learning algorithm Location Propagation Gaze Estimation

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A neuro-fuzzy approach for segmentation of human objects in image sequences

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2003年第3期33卷 420-437页

作者： Lee, SJ Ouyang, CS Du, SH Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video streams of multimedia applications. Existing segmentation methods apply simple criteria to detect human objects, leading to the restriction of the usage or a high segmentation error. We combine temporal and spatial information and. employ a neuro-fuzzy mechanism to overcome these difficulties. A fuzzy self-chistering technique is used to divide the I base frame of a video stream into a set of segments which are then categorized as foreground or background based on a combination of multiple criteria. Then, human objects in the base frame and the remaining frames of the video stream are precisely located by a fuzzy neural network constructed with the fuzzy rules previously obtained and is trained by a singular value decomposition (SVD)-based hybrid learning algorithm. The proposed approach has been tested on several different video streams, and the results have shown that the approach can produce a much better segmentation than other methods.

关键词： fuzzy flustering fuzzy neural network learning algorithm video compression video objects

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