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Application of Computation intelligence Techniques for Energy Load and Price Forecast in some States of USA

Application of Computation Intelligence Techniques for Energ...

引用

ieee International symposium on Intelligent signal processing

作者： Joao C. Mourao Antonio E. Ruano Centre for Intelligent Systems Faculty of Sciences and Technology Campus de Gambelas University of Algarve Faro Portugal

ISBN: (纸本)9781424408290;1424408296

The purpose of this paper is to forecast the load and the price of electricity, 49 hours ahead. To accomplish these goals, computational intelligence techniques were used, specifically artificial neural networks and genetic algorithms. The neural networks employed are RBFs (radial basis functions), fully connected and with just one hidden layer. The genetic algorithm used was MOGA (multiple objective genetic algorithm), which, as the name indicates, minimizes not a single objective but several. The neural networks are trained for one step ahead, and its output is feedback until 49 hours are calculated. MOGA is used for the input selection and for topology determination. The data used was kindly given by the University of Auburn, USA, and refers to real data from some North-American states.

关键词： Computer applications Competitive intelligence Load forecasting Artificial neural networks Genetic algorithms computational and artificial intelligence computational intelligence Output feedback Neurofeedback Network topology

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Solving Classification Problems Using Supervised Self-Organizing Map

Solving Classification Problems Using Supervised Self-Organi...

引用

ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： Arit Thammano Jiraporn Kiatwuthiamorn Computational Intelligence Laboratory Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self- organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging.

关键词： Neural networks Artificial neural networks Information technology Fuzzy neural networks Predictive models signal processing computational intelligence Laboratories Electronic mail Euclidean distance

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Feature Selection for Change Detection in Multivariate Time-Series

Feature Selection for Change Detection in Multivariate Time-...

引用

ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Michael Botsch Josef A. Nossek Institute for Circuit Theory and Signal Processing Technical University Munich Munich Germany

In machine learning the preprocessing of the observations and the resulting features are one of the most important factors for the performance of the final system. In this paper a method to perform feature selection for change detection in multivariate time-series is presented. Feature selection aims to determine a small subset which is representative for the change detection task from a given set of features. We are dealing with time-series where the classification has to be done on time-stamp level, although the smallest independent entity is a scenario consisting of one or more time-series. Despite this difficulty we will show how feature selection based on the generalization ability of a classifier can be realized by defining a cost function on scenario level. For the classification step in the feature selection process a modified random forest (RF) algorithm - which we will call scenario based random forest (SBRF) - is used due to its intrinsic possibility to estimate the generalization error. The excellent performance of the proposed feature selection algorithm will be shown in a car crash detection application

关键词： Machine learning Sequences Circuit theory signal processing Radio frequency signal processing algorithms Stochastic processes Feature extraction computational intelligence Data mining

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Foundations of Immunocomputing

Foundations of Immunocomputing

引用

ieee symposium on Foundations of computational intelligence, FOCI

作者： Alexander Tarakanov Giuseppe Nicosia St. Petersburg Institute for Informatics and Automation Russian Academy of Science Saint Petersburg Russia Department of Mathematics and Computer Science Universita Catania Catania Italy

This paper presents the mathematical basis of the immunocomputing using feature extraction and pattern recognition. The key notions of the approach are the formal immune network (FIN) and the coding theory for machine learning. The training of FIN includes apoptosis (programmed cell death) and auto immunization both controlled by cytokines (messenger proteins), whereas parameters of FIN can be optimized by Kullback entropy. Recent results suggest that the approach outperforms (by training time and accuracy) state-of-art approaches of computational intelligence

关键词： Immune system Biology computing computational intelligence Pattern recognition Feature extraction Proteins Computer networks Artificial neural networks Application specific integrated circuits Biomedical signal processing

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GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data

GAIS: A Method for Detecting Interleaved Sequential Patterns...

引用

ieee symposium on computational intelligence and Data Mining (CIDM)

作者： Marja Ruotsalainen Timo Ala-Kleemola Ari Visa Institute of Signal Processing Tampere University of Technology Tampere Finland

This paper introduces a novel method, GAIS, for detecting interleaved sequential patterns from databases. A case, where data is of low quality and has errors is considered. Pattern detection from erroneous data, which contains multiple interleaved patterns is an important problem in a field of sensor network applications. We approach the problem by grouping data rows with the help of a model database and comparing groups with the models. In evaluation GAIS clearly outperforms the greedy algorithm. Using GAIS desired sequential patterns can be detected from low quality data.

关键词： Databases Pattern matching signal processing Greedy algorithms Redundancy Temperature sensors Temperature measurement Ant colony optimization Particle swarm optimization Genetic algorithms

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Face Recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

引用

ieee symposium on computational intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms Pattern recognition Artificial intelligence Particle swarm optimization computational intelligence Computer security Application software image processing

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A Decision Making Framework for Dressing Consultant

A Decision Making Framework for Dressing Consultant

引用

ieee symposium on computational intelligence in Multi-Criteria Decision-Making, MCDM

作者： Ching-I Cheng Damon Shing-Min Liu Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

The project, Dressing Consultant, aims to provide a system which functions as a personal wearing advisor to help general users choose a correct clothing for occasions. ALCOVE (attention learning covering network) neural network model is used to train the matchmaker as a fashion editor. In addition, image processing techniques are employed at pre-processing stage to obtain the essential data of garments and to build a digital wardrobe for individuals. On the occasions when user has trouble finding an outfit for a special event, what user could do is to make a decision of the style of apparel to the system and let the system go through piece of garments in the digital wardrobe, and the matchmaker will then find several matched pairs. Eventually, the most similarly suitable and matched garments pair is shown in 3D show room. This paper focuses on making decision of correct clothing according to those classifying and matching rules extracted from fashion industry

关键词： Decision making Psychology Computer science Artificial intelligence computational intelligence Neural networks image processing Clothing industry Textile industry Layout

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Adaptive Enhancement of Historical Document images

Adaptive Enhancement of Historical Document Images

引用

ieee International symposium on signal processing and Information Technology (ISSPIT)

作者： N. Krishna Kishore Priti P. Rege Department of Electronics and Telecommunications College of Engineering Pune India

In this paper we present a simple and computationally efficient method for document image enhancement. We use Unsharp masking to enhance the edge detail information in the degraded document. This image is then used to adjust the local threshold for each pixel. Proposed method is experimentally compared with Laplacian sign method and the Otsu method. It is shown that the method improves sharpness of the image with nearly half computational time.

关键词： Histograms image storage Entropy Degradation Laplace equations Background noise Lighting image enhancement Pixel Noise shaping

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Map Building and Localization of a Robot Using Omnidirectional image Sequences

Map Building and Localization of a Robot Using Omnidirection...

引用

International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Zoltan Vamossy Budapest Tech John von Neumann Institute of Software Technology Faculty of Informatics Institute of Software Technology

The paper describes a map building module, where the image sequences of the omnidirectional camera are transformed into virtual top-view ones and melted into the global dynamic map. After learning the environment from training images, a current image is compared to the training set by appearance-based matching. Appropriate classification strategies yield an estimate of the robot's current position.

关键词： image sequences Robot vision systems Cameras Orbital robotics Optical imaging Optical surface waves Navigation signal processing algorithms Informatics Yield estimation

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Opposite Transfer Functions and Backpropagation Through Time

Opposite Transfer Functions and Backpropagation Through Time

引用

ieee symposium on Foundations of computational intelligence, FOCI

作者： Mario Ventresca Hamid R. Tizhoosh Pattern Analysis and Machine Intelligence (PAMI) laboratory Systems Design Engineering Department University of Waterloo Waterloo ONT Canada

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

关键词： Transfer functions Backpropagation algorithms signal processing algorithms Recurrent neural networks Acceleration Convergence computational intelligence Stability Nonlinear dynamical systems computational complexity

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