Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, na...

A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and F...

One of the critisisms of rule-based fuzzy controllers has been their use of only shallow knowledge bases. This shallowness is primarily attributable to the expressiveness of rule-based systems, which is that of the Horn clause subset of rst-order predicate calculus (FOPC). We would argue that the language in which declarative knowledge is represented must be at least as expressive as the full F...

A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system...

In this paper, we propose a complete and efficient algorithm to perform the fuzzy inference of a rule-based system modeled by Fuzzy Petri Nets (FPN). The algorithm is developed to simulate the inference process from the starting propositions to the goal propositions. The structures of the FPN are in more generic forms, compared to those applied in previous papers. The formal description of mode...

Control Chart Patterns (CCPs) recognition is one the most important concepts in control chart application. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this study, a fuzzy rule-based system is developed for X ̅ control charts to prioritize the control chart causes based on the accumulated e...

In this paper, a new VLSI architecture is provided for the application of quad-input and dual-output Fuzzy Logic Controller (FLC) with maximum seven fuzzy membership functions. Our approach is based on classical three stage implementation process – fuzzification, rule inference and defuzzification cores. An innovative design methodology is proposed by splitting the process between DSP processor...

In this paper a new fuzzy method for 2D texture classification is proposed. A fuzzy rule base system is designed based on computed Geometric Moments (GMs), which are rotation, scale and translation invariant, for each texture class. Although GMs are applied to classify texture, utilizing fuzzy inference system (FIS) to make decision, develops much more robust classifier. Where GMs are inputs an...

in this context and for helping to manufacturing industries in this research an attempt has been made to provide a method to managers of evaluating and ranking of agility strategies by using of a fuzzy inference system which is a branch of artificial intelligence. this research has been performed in three sequential phases. firstly, some variables, as factors of agility drivers, agility capabil...

This paper proposes a fuzzy inference based neural network for the forecasting of short term loads. The forecasting model is the integration of fuzzy inference engine and the neural network, known as Fuzzy Inference Neural Network (FINN). A FINN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that th...