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Fuzzy

In: Computers and Technology

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Lógica Fuzzi

Americana – 2014
Resumo

Considerando os problemas reais que cercam a sociedade hoje tanto nas indústrias, no comércio ou mesmo no dia a dia das pessoas, fica claro a ausência de certezas absolutas quanto a alguns aspectos. Heisenberg em 1927 já falava sobre o princípio da incerteza que serviu como alicerce principal da teoria quântica. Este princípio mais tarde iria auxiliar no desenvolvimento da lógica fuzzy, onde sua forma de raciocinar é muito semelhante ao modelo de raciocínio humano, baseado em aproximações e cercado de incertezas e suposições.
Esses algoritmos são amplamente utilizados atualmente em diversas áreas como: robótica, automação de linhas de produção, simulações financeiras entre outras. O sistema lógico apresentado pela lógica fuzzy quando aplicado vai além do raciocínio booleano, pois busca atribuir graus para os elementos em questão de forma que a resposta contido ou não contido somente, não satisfaz e busca-se saber o quão contido ou o quão não contido esta determinado elemento.

Sumário

Introdução...................................................................................................................................1
O que é Lógica Fuzzi..................................................................................................................2
Raciocínio Dedutivo...................................................................................................................4
Raciocínio Indedutivo.................................................................................................................4
Conclusão....................................................................................................................................5
Referências Bibliográficas..........................................................................................................6

Introdução

O sistema da lógica Fuzzy resolve problemas em termos de valores binários, manipulando muito bem os sistemas em que ha problemas de decisão a serem tomadas; como por exemplo: mais, menos, maior, menor entre outros, com uma forma bem parecida com a do ser humano nessa tomada de decisão.
Devido a inúmeras possibilidades é a técnica padrão e tem uma ampla aceitação para controles em processo industriais mesclando conceitos da logica clássica e os conjuntos Lukasiewics. Amplamente indicada para solução de problemas reais onde são necessários soluções não necessariamente ótimas, sua característica principal e manusear informações imprecisas, onde tem um método interessante de tradução e compreensão de expressões verbais que são típicas na comunicação humana. O seu procedimento de raciocínio é paralelo, ou seja, a combinação da parte antecedente da regra com a entrada pode ser computada de forma paralela, fazendo com que a logica fuzzy seja apropriado para ser implementado em processadores paralelos.
Essa lógica aproxima-se muito da decisão humana deixando que a parte computacional tome decisões que vão além do sim e não, ou seja, toma-se decisões abstratas.
No seu raciocínio dedutivo usa-se as informações que já se tem conhecimento essa logica, captura esse conhecimento e o transforma ao de um operador humano. No raciocínio indutivo o processo seria mais dinâmico através de observações de comportamentos nesse caso o controlador consegue identificar situações que se encontram repetidamente sabendo gerencia o mesmo sempre que necessário.

O que é Lógica Fuzzi

As primeiras noções da lógica dos conceitos “vagos” foi desenvolvida por Jan Lukasiewicz em 1920 que introduziu conjuntos com graus de pertinência sendo 0, ½ e 1 e, mais tarde, expandiu para um número infinito de valores entre 0 e 1.
Em 1965, o professor da Universidade da Califórnia Lotfi Asker Zadeh criou a lógica “fuzzy” combinando os conceitos da lógica clássica e os conjuntos de Lukasiewicz, definindo graus de pertinência. Ele observou que recursos tecnológicos, baseados na lógica booleana, não eram suficientes para automatizar atividades relacionadas a problemas de natureza industrial, biológica ou química.
Entre 1970 e 1980 as aplicações industriais da lógica “fuzzy” aconteceram com maior importância na Europa. Em 1974, o Prof. Ebrahim Mamdani conseguiu controlar uma máquina a vapor com tipos diferentes de controladores aplicando o raciocínio fuzzy.
E após 1980, o Japão iniciou seu uso com aplicações na indústria. Algumas das primeiras aplicações foram em um tratamento de água feito pela Fuji Electric em 1983 e pela Hitachi em um sistema de metrô inaugurado em 1987.
Devido ao desenvolvimento e as inúmeras possibilidades práticas dos sistemas “fuzzy” e o grande sucesso comercial de suas aplicações, a lógica “fuzzy” é considerada hoje uma técnica “standard” e tem uma ampla aceitação na área de controle de processos industriais.
A característica especial da lógica fuzzy (também referida como lógica nebulosa e em alguns casos por Teoria das Possibilidades) é a de representar uma forma inovadora de manuseio de informações imprecisas, de forma muito distinta da Teoria das Probabilidades. A lógica fuzzy possui um método interessante de compreensão e tradução de expressões verbais, ações cotidianas de funcionamento racional, vagas, imprecisas e qualitativas, típicas na comunicação humana em valores numéricos. Essa simulação do real faz com que os computadores possam entender a experiência humana. Assim a tecnologia possibilitada pelo enfoque fuzzy tem um imenso valor prático, na qual se torna possível a inclusão da experiência de operadores humanos, os quais controlam processos e plantas industriais, em controladores computadorizados, possibilitando estratégias de tomadas de decisões em problemas complexos, em outras palavras o usuário da lógica fuzzy ensina sua máquina a simular pensamentos imprevistos e talvez não programados.
A Lógica Fuzzy consiste em aproximar a decisão computacional da decisão humana, tornando as máquinas mais capacitadas a seu trabalho. Isto é feito de forma que a decisão de uma máquina não se resuma apenas a um "sim" ou um "não", mas também tenha decisões "abstratas", do tipo "um pouco mais", "talvez sim", e outras tantas variáveis que representem as decisões humanas. É um modo de interligar inerentemente processos analógicos que se deslocam através de uma faixa contínua para um computador digital que podem ver coisas com valores numéricos bem definidos (valores discretos).
Uma das principais potencialidades da Lógica Fuzzy, quando comparada com outros esquemas que tratam com dados imprecisos como redes neurais, é que suas bases de conhecimento, as quais estão no formato de regras de produção, são fáceis de examinar e entender. Este formato de regra também torna fácil a manutenção e a atualização da base de conhecimento.
A lógica fuzzy teve um sucesso mundial no uso de sua modelagem e controle, devido sua utilização como ferramenta de controle industrial, manufatura, comunicação homem-máquina e em sistemas de tomadas de decisões, talvez os mais usados.

Raciocínio Dedutivo

Processo que as pessoas utilizam para inferir conclusões baseadas em informações já conhecidas. Operadores humanos podem controlar processos industriais e plantas com características não lineares e até com comportamento dinâmico pouco conhecido, através de experiência e inferência de relações entre as variáveis do processo. A lógica fuzzy pode capturar esse conhecimento em um controlador fuzzy, possibilitando a implementação de um controlador computacional com desempenho equivalente ao do operador humano.

Raciocínio Indutivo

Também pode ser utilizado no projeto de controladores fuzzy, onde seria possível o aprendizado e generalização através de exemplos particulares provenientes da observação do comportamento do processo numa situação dinâmica, ou variante no tempo. Este enfoque geralmente é referido como controle fuzzy "aprendiz", ou então como controle fuzzy adaptativo. Vantagens significantes podem ser obtidas de controladores que podem aprender com a experiência de tal forma que quando uma situação é encontrada repetidamente, estes controladores saberão como gerenciar o problema. Os sitemas fuzzy adaptativos podem se ajustar às mudanças no ambiente devido à sua capacidade de aprender e explicar seu raciocínio, além de poderem ser modificados e estendidos. Tal equilíbrio entre a aprendizagem por exemplos e a codificação do conhecimento humano explícito, fazem que tais sistemas sejam muito robustos, extensíveis e passíveis de serem aplicados em uma larga gama de problemas.

Conclusão

Este trabalho introduziu os conceitos inerentes à lógica fuzzy e sua aplicação na solução de problemas reais. Após esses estudos conclui-se que a lógica fuzzy é amplamente indicada para solução de problemas reais onde é necessário soluções não necessariamente ótimas. A possibilidade de se gerar saídas reais quando as variáveis de entrada não necessariamente são reais e exatas permite fazer inferências que jamais seriam possíveis utilizando-se da lógica tradicional.
Outro ponto a se destacar é que a análise do problema é bastante importante para decidir se deve utilizar a lógica fuzzy ou uma lógica boolena, pois dependendo as características do problema a lógica booleana pode ser mais indicada.
Por fim, no que diz respeito à lógica fuzzy em Inteligência Artificial fica claro a grande aplicabilidade desta por se assemelhar a forma humana de raciocinar e tomar decisões.

Referências

W. PEDRYCZ and F. GOMIDE; “Fuzzy Systems Engineering: Toward Human-Centric Computing”; Wiley/IEEE Press, 2007
C. J. HARRIS, C. G. MOORE & M. BROWN; “Intelligent control: Aspects of Fuzzy Logic and Neural Nets”; World Scientific, 1993
KOSKO, Bart; “Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence”; Prentice-Hall International, 1992
EARL COX; “The Fuzzy Systems Handbook: a Practitioner's Guide to Building, Using and Maintaining Fuzzy Systems”; Professional, 1994
PINHO, Alexandre F. Uma contribuição para a resolução de problemas de programação de operações em sistemas de produção intermitentes flow-shop: A consideração de incertezas. 1999. Dissertação (Mestrado em Engenharia) – Universidade Federal de Itajubá, Itajubá, 1999.
Lógica Fuzzy. Wikipédia a enciclopédia livre. Disponível em: http://pt.wikipedia.org/wiki/L%C3%B3gica_difusa. Acesso em 09/05/2014.…...

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...A Fuzzy Expert System for Task Distribution in Teams under Unbalanced Workload Conditions José A. Benito Calleja and Jimmy Troost Thales Nederland, Hengelo, The Netherlands jose.benito@nl.thalesgroup.com, jimmy.troost@nl.thalesgroup.com Abstract Inappropriate workload levels on the team members of a naval force have been detected as a problem that can threaten the performance and safety of future naval operations. A suitable distribution of tasks among the members of a team is a crucial issue in order to prevent high and low workload levels. In this paper, we propose a rule-based expert system, the Task Distribution Expert System (TDES), which assists team leaders to manage mental workload in a team by suggesting appropriate task assignments. The TDES emulates the behavior of a team leader deciding which member of the team should perform a task and how. The system handles mental workload as an uncertain fuzzy concept comprising three fuzzy variables that represent the way mental workload affects performance. Automation issues and different recommendations for effective workload management in teams are analyzed and incorporated. A prototype demonstrates the system. 1. Introduction Naval Command and Control (C2) systems support organizations formed by a number of people cooperating on a multitude of tasks simultaneously to achieve overall goals. Future naval C2 systems are characterized by less people, more information available, shorter......

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