Soft Computing Lecture 20 Review of HIS

Combined Numerical and Linguistic Knowledge Representation and Its Application to Medical Diagnosis (P.. Meesad,G. G. Yen, IEEE TRANS. ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 33, NO. 2, MARCH 2003)

Architecture Incremental learning fuzzy neural network (ILFN) Fuzzy expert systems (FES) Network-to-rule module; Rule-to-network module; Decision-explanation module.

ILFN The ILFN is a self-organizing network that is equipped with an online, incremental learning algorithm capable of learning all training patterns within only one pass. The system can be viewed as two subsystems: –an input subsystem, –a target subsystem. Each subsystem has three layers: –one input layer, –one hidden layer, –output layer. The hidden layer of both the input subsystem and the target subsystem are linked together via a controller module which is used to control the growing neurons in the hidden layer. Each output layer of both subsystems consists of two modules. The output layer of the input subsystem consists of a pruning module and a membership module, while the output layer of the target subsystem consists of a pruning module and a target module. The membership module of the input subsystem and the target module of the target subsystem are simultaneously updated with their number of neurons controlled by the pruning modules. The output of the classifier is linked together via a decision layer.

FES In a FES, a knowledge base is used for an explanation purpose as well as in a decision making process. A knowledge structure used in the proposed FES is comprised of the following: –input features’ names; –variables’ ranges; –number of linguistic labels; –linguistic labels; –membership function; –membership functions’ parameters; –fuzzy if-then rules. The information about the knowledge structure of the FES can be provided by experts or automatically generated from data.

FES (2) In developing a FES, developers must pay attention to several issues such as accuracy, comprehensibility, compactness, completeness, and consistency. Accuracy is a quantitative measure that indicates the performance of a FES in classifying both training and testing data. Comprehensibility indicates how easily a FES can be accessible by human beings. Generally, comprehensibility of a FES depends on the following aspects: the distinguishability of the shapes of membership functions, the number of fuzzy if-then rules, and the number of antecedent conditions of fuzzy if-then rules. Compactness involves the size of fuzzy if-then rules and the number of antecedents of fuzzy if-then rules. More compactness of a fuzzy system usually yields a higher comprehensibility. Completeness assures that a FES will provide a nonzero output for any given input in the input space. Consistency makes sure that fuzzy if-then rules are not conflicting with each other as well as those from human senses. Fuzzy if-then rules are inconsistent if they have very similar antecedents, but distinct consequents, and they conflict with the expert knowledge. If there are fuzzy if-then rules that are conflicting to each other, the rules become unclear [43]–[46]. The conflicting rules need to be resolved.

Form of rules in Knowledge base Rule 1: If feature is high and feature is low and feature is medium, then class is 1; Rule 2: If feature is medium and is low and feature is low, then class is 3; Rule 3: If feature is high and feature is medium and feature is medium, then class is 2.

(a) Projection of ILFN to 1-D fuzzy sets. (b) FES grid partition with its parameter projected from trained

The ILFN groups the patterns in the input space into a small number of clusters. Based on grid partition methods, the clusters and its parameters of the trained ILFN can be mapped to fuzzy if-then rules. The number of fuzzy if-then rules is equal to the number of clusters in the trained ILFN. The number of fuzzy sets in each dimension depends on the number of grid partitions chosen. Using a grid-based projection method, the fuzzy if–then rules of the FES are extracted from a trained ILFN. The hidden numerical weights of the ILFN are mapped into initial fuzzy if–then rules. A genetic algorithm is then used to select only discriminatory features resulting in a more compact rule set with highly transparent linguistic terms.

Mapping from ILFN to linguistic rules.

A Hybrid Architecture for Learning Robot Control Tasks (Manfred Huber and Roderic A. Grupen, 1999 AAAI Spring Symposium Series: Hybrid Systems and AI)

Vibration diagnostic hybrid system

Vibration diagnostic hybrid system (2) The diagnostic hybrid system is designed to detect and report bearing faults in heavy rotating machinery such as large motors and fans. The diagnostic information is based upon vibration data gathered from sensors connected to the various items of plant under observation. A great deal of preprocessing must be carried out since the raw vibration data has a high dimensionality and only a small fraction can be used to train the neural networks. The preprocessing module reduces the dimensionality of the raw input spectra by selecting the most important parameters, which are easily calculated using heuristics. The transformed data is then passed onto the neural network module, which is designed to detect a number of bearing faults. A neural network is required for this task since several faults exhibit the same symptoms. The output of the neural network is interpreted by a rule-based diagnosis module which provides details of the faults and is also able to provide trend analysis.

Hybrid system developed by Tirri was developed to assist the inferencing engine of an expert system by a set of trained neural networks. The hybrid system requires parallel interaction to occur between its modules and therefore employs active coupling. The system consists of three knowledge bases: a rule base, a fact base which contains the working memory and a neural network of several trained radial basis function networks (RBF). Each neural network corresponds to a specic symbolic predicate in the system.

Neural network based inferencing engine (2)

CBR/NN data analysis system Case-based reasoning (CBR) module Interleaved with several neural network modules. The system is intended as a tool for data mining and knowledge discovery within large databases. It has been used successfully in two large real-world domains: Analyzing oceanographic data for the prediction of ocean parameters and a civil engineering design project.

CBR/NN data analysis system (2)

ES for analyzing of ECG

An Intelligent System for Failure Detection and Control in an Autonomous Underwater Vehicle (N. Ranganathan, Minesh I. Patel, and R. Sathyamurthy, 2001 IEEE)