Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

A Protocol and Tool for Developing a Descriptive Behavioral Model

Fuzzy rules have been used to model complex human behavior in order to develop sophisticated industrial control systems. The use of fuzzy rules to create a behavioral model provides a quantitative basis for discussing the contribution of elements of the model to theories about the behavior. The application of a protocol and tool simplifies the development of a behavioral model from observational data. Extraction of a high level, linguistic behavioral model from the observational data is used to discover knowledge about the data. Tuning of the model is accomplished by parameter optimization through the adjustment of membership functions using the genetic fuzzy, self-adaptive system. A case study demonstrating the use of the protocol and tool is presented. In the study, a behavioral model is developed that integrates the analysis of the observational data with Social Network Analysis. The integrated behavioral model provides an effective platform for a quantitative analysis of the activities impacting behavior.  M.S