197,094 research outputs found
M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing
No full textComplex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS
ADEPT-Advanced Decision Environment for Process Tasks: Overview & Architecture
No full textThis paper provides an introduction to project ADEPT (Advanced Decision Environment for Process Tasks). The project is researching both the technology and the methods that are needed to improve the way information is gathered, managed, distributed and presented to people in key business functions and operations. The paper presents a first-level breakdown of the central architecture concepts that have been emerging during the first six months of the ADEPT project. The use of autonomous agents to provide information services is explored and a language for their communication and representation developed. The work presented here represents only the preliminary steps towards the final architecture and is likely to undergo significant refinement throughout the project life-cycle
Designing a Re-Usable Coordination Module for Cooperative Industrial Control Applications
No full textDistributed artificial intelligence (DAI) systems, in which multiple agents communicate and co-operate with one another to achieve their individual and collective goals, are a promising enabling technology for constructing large, real world industrial control applications. To facilitate the development of such systems a number of generic DAI frameworks have been devised. These frameworks typically aid the development process by providing a language, a set of structures, and/or some tools with which the necessary infrastructure and support mechanisms for interacting agents can be instantiated. The paper reports on one such framework, called ARCHON, which has been used to build DAI systems in the following industrial control domains: electricity distribution management, electricity transportation management, cement factory control, particle accelerator control and flexible assembly robotic cells. A distinguishing and novel feature of the ARCHON framework is that it extends the level of support offered to the system builder - it provides generic and reusable knowledge about the process of cooperation, in addition to the more standard development facilities. This generic knowledge is embedded in a domain-independent co-ordination module and it is the rationale, design, implementation and evaluation of this module which forms the major contribution of the paper
Designing and Implementing a Multi-Agent Architecture for Business Process Management
No full textThis paper presents a general multi-agent architecture for the management of business processes, and an agent design that has been implemented within such a system. The autonomy of the agents involved in the system is considered paramount. Therefore, for agents to agree on the distribution of problem solving effort within the system they must negotiate. The knowledge sharing and negotiation functions of such an agent are focused on in this paper
Um modelo de rede neuro-fuzzy baseada em funções de base radial capaz de inferir regras do tipo Mamdani
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Este trabalho tem como objetivo apresentar um novo sistema de inferência neuro-fuzzy, chamado RBFuzzy, capaz de extrair conhecimento a partir de dados e gerar regras fuzzy do tipo Mamdani com alta interpretabilidade. A RBFuzzy é um sistema de inferência neuro-fuzzy que aproveita o comportamento funcional de neurônios ativados por Funções de Base Radial (RBF) e sua relação com sistemas de inferência fuzzy. A arquitetura da rede RBFuzzy permite extrair um conjunto de regras linguísticas a partir da estrutura conexionista e dos pesos ajustados de uma rede neural. Uma extensão do algoritmo da otimização da colônia de formigas (ACO, do inglês ant colony optimization algorithm) é utilizada para ajustar os pesos de cada regra para gerar um conjunto de regras fuzzy acurado e interpretável. Tendo um conjunto de regras fuzzy um especialista pode adicionar regras novas para incorporar conhecimento novo ao modelo de previsão gerado e também corrigir regras que foram geradas por dados imprecisos.Abstract : This work presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given oshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understandingthe operation of the system by a human expert
M-CFIS-R: Mamdani complex fuzzy inference system with rule reduction using complex fuzzy measures in granular computing
No full textComplex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS
Fuzzy systems of Mamdani type in the LU representation
No full textLU parametric representation of fuzzy numbers describes fuzzy sets via the lower and upper endpoints of the level sets. This representation is used in the present paper to develop a single input single output fuzzy system of Mamdani type. The functions returning the endpoints of the level sets of a Mamdani system are calculated and the defuzzification is also described in the new LU-setting. The advantage provided by the LU representation is a reduced computational complexity, yet providing the same output as a Mamdani system, making in this way Mamdani systems directly and easily usable in real-time interactive simmulation
ADEPT: Managing Business Processes Using Intelligent Agents
No full textThis paper describes work undertaken in the ADEPT (Advanced Decision Environment for Process Tasks) project towards developing an agent-based infrastructure for managing business processes. We describe how the key technology of negotiating, service providing, autonomous agents was realised and demonstrate how this was applied to the BT business process of providing a customer quote for network services. Issues of agent visualisation are also addressed
Citizen and subject: Contemporary Africa and the legacy of late colonialism
No full textMetadata only recordIn analyzing the obstacles to democratization in post-independence Africa, Mahmood Mamdani offers a bold, insightful account of colonialism's legacy--a bifurcated power that mediated racial domination through tribally organized local authorities, reproducing racial identity in citizens and ethnic identity in subjects. Many writers have understood colonial rule as either direct (French) or indirect (British), with a third variant--apartheid--as exceptional. This benign terminology, Mamdani shows, masks the fact that these were actually variants of despotism. While direct rule denied rights to subjects on racial grounds, indirect rule incorporated them into a customary mode of rule, with state-appointed Native Authorities defining custom. By tapping authoritarian possibilities in culture, and by giving culture an authoritarian bent, indirect rule (decentralized despotism) set the pace for Africa; the French followed suit by changing from direct to indirect administration, while apartheid emerged relatively later. Apartheid, Mamdani shows, was actually the generic form of the colonial state in Africa. Through case studies of rural (Uganda) and urban (South Africa) resistance movements, we learn how these institutional features fragment resistance and how states tend to play off reform in one sector against repression in the other. Reforming a power that institutionally enforces tension between town and country, and between ethnicities, is the key challenge for anyone interested in democratic reform in Africa
An integrative functional genomics approach for discovering biomarkers in schizophrenia
No full textSchizophrenia (SZ) is a complex disorder resulting from both genetic and environmental causes with a lifetime prevalence world-wide of 1%; however, there are no specific, sensitive and validated biomarkers for SZ. A general unifying hypothesis has been put forward that disease-associated single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) are more likely to be associated with gene expression quantitative trait loci (eQTL). We will describe this hypothesis and review primary methodology with refinements for testing this paradigmatic approach in SZ. We will describe biomarker studies of SZ and testing enrichment of SNPs that are associated both with eQTLs and existing GWAS of SZ. SZ-associated SNPs that overlap with eQTLs can be placed into gene-gene expression, protein-protein and protein-DNA interaction networks. Further, those networks can be tested by reducing/silencing the gene expression levels of critical nodes. We present pilot data to support these methods of investigation such as the use of eQTLs to annotate GWASs of SZ, which could be applied to the field of biomarker discovery. Those networks that have association with SNP markers, especially cis-regulated expression, might lead to a more clear understanding of important candidate genes that predispose to disease and alter expression. This method has general application to many complex disorders
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