Phayung Meesad

Pattern classification by a neurofuzzy network: application to vibration monitoring.

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fishers Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.

CONSTRUCTING A FUZZY RULE-BASED SYSTEM USING THE ILFN NETWORK AND GENETIC ALGORITHM

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fishers Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

A hybrid intelligent system for medical diagnosis

We propose a novel hybrid intelligent system (HIS) that is a combination of numerical and linguistic knowledge representation. The proposed HIS is a hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a fuzzy linguistic model optimized via the genetic algorithm. The ILFN is self-organizing network with the capability of fast, online, incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy and comprehensibility. The resulted HIS is capable of dealing with low-level numerical computation and higher-level linguistic computation. After the system completely constructed, it can incrementally learn new information in both numerical and linguistic structures. To evaluate the systems performance the well-known benchmark Wisconsin breast cancer data was studied as an application to medical diagnosis. The simulation results show that the proposed HIS perform better than the individual standalone systems.

Quantitative measures of the accuracy, comprehensibility, and completeness of a fuzzy expert system

Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES), focusing only on its accuracy without considering comprehensibility may result in a system that is not easy to understand or the so called a black box model. To exploit the transparency features of FESs for explanation in higher-level knowledge representation, a FES should provide high comprehensibility while preserving its accuracy. The completeness of fuzzy sets and rule structures should also be considered to guarantee that every data point has a response output. This paper proposes some quantitative measures to determine the degree of the accuracy, comprehensibility, and completeness of FESs. These quantitative measures are then used as a fitness function for a genetic algorithm in an optimally built FES.

Constructing a fuzzy expert system using the ILFN network and the genetic algorithm

A method for automatic construction of a fuzzy expert system from numerical data using the ILFN network and a genetic algorithm is presented. The incremental learning fuzzy neural (ILFN) network was developed for pattern classification problems. The ILFN network is a fast, one-pass, on-line, and incremental learning algorithm. A knowledge base for fuzzy expert systems is extracted from the hidden units of the ILFN classifier. The genetic algorithm is then used, in an iterative manner, to reduce the number of rules and select important input pattern features needed to generate a comprehensible fuzzy rule-based system.

An effective neuro-fuzzy paradigm for machinery condition health monitoring

A new learning algorithm suitable for pattern classification in machine condition health monitoring based on fuzzy neural networks called an incremental learning fuzzy neuron network (ILFN) has been developed. The ILFN, using Gaussian neurons to represent the distributions of the input space, is an online one-pass incremental learning algorithm. The network is a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. To prove the concept, the simulations have been performed with vibration data. Furthermore, the classification performance of the network has been tested on other benchmark data sets, such as the iris data and a vowel data set. For the generalization capability, comparison studies among other well-known classifiers were performed and the ILFN was found competitive with or even superior to many existing classifiers. Additionally, the ILFN uses far less training time than conventional classifiers.

Accuracy, comprehensibility and completeness evaluation of a fuzzy expert system

Using optimization tools such as genetic algorithms to construct a fuzzy expert system (FES) focusing only on its accuracy without considering the comprehensibility may not result in a system that produces understandable expressions. To exploit the transparency characteristics of FES for reasoning in a higher-level knowledge representation, a FES should provide high comprehensibility while preserving its accuracy. The completeness of fuzzy sets and rule structures should also be considered to guarantee that every data point has a response output. This paper proposes some quantitative measures for a FES to determine the degree of the accuracy, the comprehensibility of the fuzzy sets, and the completeness of fuzzy rule structure. These quantitative measures are then used as a fitness function for a genetic algorithm in optimally refining a FES.

Pattern classification by an incremental learning fuzzy neural network

A new learning algorithm suitable for pattern classification in machine condition health monitoring based on fuzzy neural networks called an incremental learning fuzzy neuron network (ILFN) has been developed. The ILFN, using Gaussian neurons to represent the distributions of the input space, is an online one-pass incremental learning algorithm. The network is a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. To prove the concept the simulations have been performed with the vibration data known as Westland vibration data set. Furthermore, the classification performance of the network has been tested on other benchmark data sets, such as Fishers iris data (1936) and a vowel data set. For the generalization capability, comparison studies among other well-known classifiers were performed and the ILFN was found competitive with or even superior to many existing classifiers. Additionally the ILFN uses far less training time than conventional classifiers.

Development of a neuro-fuzzy expert system for predictive maintenance

In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

Combined numerical and linguistic knowledge representation and its application to medical diagnosis

In this study, we propose a novel hybrid intelligent system (HIS) which provides a unified integration of numerical and linguistic knowledge representations. The proposed HIS is hierarchical integration of an incremental learning fuzzy neural network (ILFN) and a linguistic model, i.e., fuzzy expert system, optimized via the genetic algorithm. The ILFN is a self-organizing network with the capability of fast, one-pass, online, and incremental learning. The linguistic model is constructed based on knowledge embedded in the trained ILFN or provided by the domain expert. The knowledge captured from the low-level ILFN can be mapped to the higher-level linguistic model and vice versa. The GA is applied to optimize the linguistic model to maintain high accuracy, comprehensibility, completeness, compactness, and consistency. After the system being completely constructed, it can incrementally learn new information in both numerical and linguistic forms. To evaluate the systems performance, the well-known benchmark Wisconsin breast cancer data set was studied for an application to medical diagnosis. The simulation results have shown that the prosed HIS perform better than the individual standalone systems. The comparison results show that the linguistic rules extracted are competitive with or even superior to some well-known methods.