Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development:the hybridisation and its effectiveness

A hybrid approach for integrating group Delphi, fuzzy logic and expert systems for developing marketing strategies is proposed in this paper. Within this approach, the group Delphi method is employed to help groups of managers undertake SWOT analysis. Fuzzy logic is applied to fuzzify the results of SWOT analysis. Expert systems are utilised to formulate marketing strategies based upon the fuzzified strategic inputs. In addition, guidelines are also provided to help users link the hybrid approach with managerial judgement and intuition. The effectiveness of the hybrid approach has been validated with MBA and MA marketing students. It is concluded that the hybrid approach is more effective in terms of decision confidence, group consensus, helping to understand strategic factors, helping strategic thinking, and coupling analysis with judgement, etc.

Contradiction versus Selfcontradiction in Fuzzy Logic

* This work is partially supported by CICYT (Spain) under project TIN 2005-08943-C02-001 and by UPM-CAM (Spain) under project R05/11240.

Application of Fuzzy Logic on Image Edge Detection

In this paper a novel method for an application of digital image processing, Edge Detection is developed. The contemporary Fuzzy logic, a key concept of artificial intelligence helps to implement the fuzzy relative pixel value algorithms and helps to find and highlight all the edges associated with an image by checking the relative pixel values and thus provides an algorithm to abridge the concepts of digital image processing and artificial intelligence. Exhaustive scanning of an image using the windowing technique takes place which is subjected to a set of fuzzy conditions for the comparison of pixel values with adjacent pixels to check the pixel magnitude gradient in the window. After the testing of fuzzy conditions the appropriate values are allocated to the pixels in the window under testing to provide an image highlighted with all the associated edges.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Fuzzy logic-based implicit authentication for mobile access control

In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper. The proposed scheme computes an aggregate score based on selected features and a threshold in real-time based on current and historic data depicting user routine. The tuned fuzzy system is then applied to the aggregated score and the threshold to determine the trust level of the current user. The proposed fuzzy-integrated implicit authentication scheme is designed to: operate adaptively and completely in the background, require minimal training period, enable high system accuracy while provide timely detection of abnormal activity. In this paper, we explore Fuzzy Logic based authentication in depth. Gaussian and triangle-based membership functions are investigated and compared using real data over several weeks from different Android phone users. The presented results show that our proposed Fuzzy Logic approach is a highly effective, and viable scheme for lightweight real-time implicit authentication on mobile devices.

Fuzzy Logic Foundations and Industrial Applications (ed. D. Ruan), Springer US, 1996, ISBN: 978-0-7923-9774-8 [Book review]

Abstract not available

Application of fuzzy logic for the evaluation of livestock slaughtering

A lógica fuzzy admite infinitos valores lógicos intermediários entre o falso e o verdadeiro. Com esse princípio, foi elaborado neste trabalho um sistema baseado em regras fuzzy, que indicam o índice de massa corporal de animais ruminantes com objetivo de obter o melhor momento para o abate. O sistema fuzzy desenvolvido teve como entradas as variáveis massa e altura, e a saída um novo índice de massa corporal, denominado Índice de Massa Corporal Fuzzy (IMC Fuzzy), que poderá servir como um sistema de detecção do momento de abate de bovinos, comparando-os entre si através das variáveis linguísticas )Muito BaixaM, ,BaixaB, ,MédiaM, ,AltaA e Muito AltaM. Para a demonstração e aplicação da utilização deste sistema fuzzy, foi feita uma análise de 147 vacas da raça Nelore, determinando os valores do IMC Fuzzy para cada animal e indicando a situação de massa corpórea de todo o rebanho. A validação realizada do sistema foi baseado em uma análise estatística, utilizando o coeficiente de correlação de Pearson 0,923, representando alta correlação positiva e indicando que o método proposto está adequado. Desta forma, o presente método possibilita a avaliação do rebanho, comparando cada animal do rebanho com seus pares do grupo, fornecendo desta forma um método quantitativo de tomada de decisão para o pecuarista. Também é possível concluir que o presente trabalho estabeleceu um método computacional baseado na lógica fuzzy capaz de imitar parte do raciocínio humano e interpretar o índice de massa corporal de qualquer tipo de espécie bovina e em qualquer região do País.

Training of interval type-2 fuzzy logic system using extreme learning machine for load forecasting

Extreme learning machine (ELM) is originally proposed for single- hidden layer feed-forward neural networks (SLFN). From the functional equivalence of fuzzy logic systems and SLFN, the fuzzy logic systems can be interpreted as a special case of SLFN under some mild conditions. Hence the fuzzy logic systems can be trained using SLFN's learning algorithms. Considering the same equivalence, ELM is utilized here to train interval type-2 fuzzy logic systems (IT2FLSs). Based on the working principle of the ELM, the parameters of the antecedent of IT2FLSs are randomly generated while the consequent part of IT2FLSs is optimized using Moore-Penrose generalized inverse of ELM. Application of the developed model to electricity load forecasting is another novelty of the research work. Experimental results shows better forecasting performance of the proposed model over the two frequently used forecasting models.

A congestion control scheme based on fuzzy logic in wireless body area networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Hybrid model for the training of interval type-2 fuzzy logic system

In this paper, a hybrid training model for interval type-2 fuzzy logic system is proposed. The hybrid training model uses extreme learning machine to tune the consequent part parameters and genetic algorithm to optimize the antecedent part parameters. The proposed hybrid learning model of interval type-2 fuzzy logic system is tested on the prediction of Mackey-Glass time series data sets with different levels of noise. The results are compared with the existing models in literature; extreme learning machine and Kalman filter based learning of consequent part parameters with randomly generated antecedent part parameters. It is observed that the interval type-2 fuzzy logic system provides improved performance with the proposed hybrid learning model.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourabilitymodel based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.