Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique

dIn this work, a perceptron neural-network technique is applied to estimate hourly values of the diffuse solar-radiation at the surface in São Paulo City, Brazil, using as input the global solar-radiation and other meteorological parameters measured from 1998 to 2001. The neural-network verification was performed using the hourly measurements of diffuse solar-radiation obtained during the year 2002. The neural network was developed based on both feature determination and pattern selection techniques. It was found that the inclusion of the atmospheric long-wave radiation as input improves the neural-network performance. on the other hand traditional meteorological parameters, like air temperature and atmospheric pressure, are not as important as long-wave radiation which acts as a surrogate for cloud-cover information on the regional scale. An objective evaluation has shown that the diffuse solar-radiation is better reproduced by neural network synthetic series than by a correlation model. (C) 2004 Elsevier Ltd. All rights reserved.

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Design and Analysis of Optical Interconnection Networks for a Dataflow Parallel Computer

This work shows the design, simulation, and analysis of two optical interconnection networks for a Dataflow parallel computer architecture. To verify the optical interconnection network performance on the Dataflow architecture, we have analyzed the load balancing among the processors during the parallel programs executions. The load balancing is a very important parameter because it is directly associated to the dataflow parallelism degree. This article proves that optical interconnection networks designed with simple optical devices can provide efficiently the dataflow requirements of a high performance communication system.

A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened Steel turning

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimization of query results in peer-to-peer database supported by semantics

The development of new technologies that use peer-to-peer networks grows every day, with the object to supply the need of sharing information, resources and services of databases around the world. Among them are the peer-to-peer databases that take advantage of peer-to-peer networks to manage distributed knowledge bases, allowing the sharing of information semantically related but syntactically heterogeneous. However, it is a challenge to ensure the efficient search for information without compromising the autonomy of each node and network flexibility, given the structural characteristics of these networks. On the other hand, some studies propose the use of ontology semantics by assigning standardized categorization of information. The main original contribution of this work is the approach of this problem with a proposal for optimization of queries supported by the Ant Colony algorithm and classification though ontologies. The results show that this strategy enables the semantic support to the searches in peer-to-peer databases, aiming to expand the results without compromising network performance. © 2011 IEEE.

Análise e desempenho de redes de acesso sem fio

Pós-graduação em Engenharia Elétrica - FEIS

Roteamento de consultas em banco de dados peer-to-peer utilizando colônias de formigas e ontologias

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento de redes neurais para previsão de cargas elétricas de sistemas de energia elétrica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Projeto otimizado de redes neurais artificiais para predição da rugosidade em processos de usinagem com a utilização da metodologia de projeto de experimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise de redes sociais e aspectos financeiros da produção de tilápias em tanques-rede na uhe de Ilha Solteira

Pós-graduação em Agronegócio e Desenvolvimento - Tupã

Performance evaluation of the fuzzy ARTMAP for network intrusion detection

Recently, considerable research work have been conducted towards finding fast and accurate pattern classifiers for training Intrusion Detection Systems (IDSs). This paper proposes using the so called Fuzzy ARTMAT classifier to detect intrusions in computer network. Our investigation shows, through simulations, how efficient such a classifier can be when used as the learning mechanism of a typical IDS. The promising evaluation results in terms of both detection accuracy and training duration indicate that the Fuzzy ARTMAP is indeed viable for this sort of application.

Using the Analytic Network Process to evaluate the relation between Reverse Logistics and corporate performance in Brazilian companies

Reverse Logistics activities are practiced by most Brazilian companies. However, a relevant problem is to identify how different Reverse Logistics programs can affect corporate performance indicators. Analytic Network Process is one of the analytical tools, which can be used to handle a multi-criteria decision-making problem and it is the only one that can capture the interdependencies between the criteria under consideration. This method was adopted here to study the influence of Reverse Logistics practices in corporate performance. Preliminary results indicated that the method can be used, reaching a result compatible to the reality of the Brazilian companies.

Evaluation of the Cartographic Communication Performance of a Route Guidance and Navigation System

This study evaluates the influence of different cartographic representations of in-car navigation systems on visual demand, subjective preference, and navigational error. It takes into account the type and complexity of the representation, maneuvering complexity, road layout, and driver gender. A group of 28 drivers (14 male and 14 female) participated in this experiment which was performed in a low-cost driving simulator. The tests were performed on a limited number of instances for each type of representation, and their purpose was to carry out a preliminary assessment and provide future avenues for further studies. Data collected for the visual demand study were analyzed using non-parametric statistical analyses. Results confirmed previous research that showed that different levels of design complexity significantly influence visual demand. Non-grid-like road networks, for example, influence significantly visual demand and navigational error. An analysis of simple maneuvers on a grid-like road network showed that static and blinking arrows did not present significant differences. From the set of representations analyzed to assess visual demand, both arrows were equally efficient. From a gender perspective, women seem to took at the display more than men, but this factor was not significant. With respect to subjective preferences, drivers prefer representations with mimetic landmarks when they perform straight-ahead tasks. For maneuvering tasks, landmarks in a perspective model created higher visual demands.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.