A novel fuzzy logic system based on N-version programming

For the consideration of different application systems, modeling the fuzzy logic rule, and deciding the shape of membership functions are very critical issues due to they play key roles in the design of fuzzy logic control system. This paper proposes a novel design methodology of fuzzy logic control system using the neural network and fault-tolerant approaches. The connectionist architecture with the learning capability of neural network and N-version programming development of a fault-tolerant technique are implemented in the proposed fuzzy logic control system. In other words, this research involves the modeling of parameterized membership functions and the partition of fuzzy linguistic variables using neural networks trained by the unsupervised learning algorithms. Based on the self-organizing algorithm, the membership function and partition of fuzzy class are not only derived automatically, but also the preconditions of fuzzy IF-THEN rules are organized. We also provide two examples, pattern recognition and tendency prediction, to demonstrate that the proposed system has a higher computational performance and its parallel architecture supports noise-tolerant capability. This generalized scheme is very satisfactory for pattern recognition and tendency prediction problems     

A neural fuzzy system with fuzzy supervised learning

A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.     

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.     

Generic form feature recognition and operation selection using connectionist modelling

For the purpose of enhancing the adaptability of computer-aided process planning systems, two connectionist modelling methods, namely neocognitron (i.e. neural network modelling for pattern recognition) and fuzzy associative memories (FAM), are applied to the phases of feature recognition and operation selection respectively in order to provide the system with the ability of self-learning and the ability to integrate traditional expert planning systems with connectionism-based models. In this paper, the attributed adjacency graph (AAG) extracted from a (B-Rep) solid model is converted to attributed adjacency matrices (AAM) that can be used as input data for the neocognitron to train and recognize feature patterns. With this technique, the system can not only self-reconstruct its recognition abilities for new features by learning without a priori knowledge but can also recognize and decompose intersection features. A fuzzy connectionist model, which is created using the Hebbian fuzzy learning algorithm, is employed subsequently to map the features to the appropriate operations. As the algorithm is capable of learning from rules, it is much easier to integrate the proposed model with conventional expert CAPP systems so that they become more generic in dealing with uncertain information processing and perform knowledge updating. mg]Keywords mw]Computer-aided process planning mw]feature recognition mw]neural networks mw]fuzzy neural networks mw]operation selection mw]connectionist model mw]fuzzy associative memories     

Adaptive fuzzy rule-based classification systems

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern is misclassified by a particular fuzzy rule, the grade of certainty of that rule is decreased. On the contrary, when a pattern is correctly classified, the grade of certainty is increased. Because the error correction-based learning procedure is not meaningful after all the given patterns are correctly classified, we cannot adjust a classification boundary in such a case. To acquire a more intuitively acceptable boundary, we propose an additional learning procedure. We also propose a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting. We can construct a compact fuzzy rule-based classification system with high performance     

Knowledge-based fuzzy MLP for classification and rule generation

A new scheme of knowledge-based classification and rule generation using a fuzzy multilayer perceptron (MLP) is proposed. Knowledge collected from a data set is initially encoded among the connection weights in terms of class a priori probabilities. This encoding also includes incorporation of hidden nodes corresponding to both the pattern classes and their complementary regions. The network architecture, in terms of both links and nodes, is then refined during training. Node growing and link pruning are also resorted to. Rules are generated from the trained network using the input, output, and connection weights in order to justify any decision(s) reached. Negative rules corresponding to a pattern not belonging to a class can also be obtained. These are useful for inferencing in ambiguous cases. Results on real life and synthetic data demonstrate that the speed of learning and classification performance of the proposed scheme are better than that obtained with the fuzzy and conventional versions of the MLP (involving no initial knowledge encoding). Both convex and concave decision regions are considered in the process.     

A neural fuzzy system with linguistic teaching signals

A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use α-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, two kinds of learning schemes are developed for the proposed system: fuzzy supervised learning and fuzzy reinforcement learning. Simulation results are presented to illustrate the performance and applicability of the proposed system     

A neuro-fuzzy system for inferencing

We justify the need for a connectionist implementation of compositional rule of inference (COI) and propose a network architecture for the same. We call it COIN—the compositional rule of inferencing. Given a relational representation of a set of rules, the proposed architecture can realize the COI. The outcome of COI depends on the choice of the implication function and also on choice of inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. The system automatically finds an “optimal” relation to represent a set of fuzzy rules. We suggest a suitable modeling of connection weights so as to ensure learned weights lie in [0, 1]. We demonstrate through numerical examples that the proposed neural realization can find a much better representation of the rules than that by usual implication and hence results in much better conclusions than the usual COI. Numerical examples exhibit that COIN outperforms not only usual COI but also some of the previous neural implementations of fuzzy logic. ©1999 John Wiley & Sons, Inc.     

浅析数据挖掘的主要方法和研究方向

随着大型数据库的不断涌现,如何从浩如烟海的数据中发现隐藏的有用知识,成为一个迫切需要研究的课题.因此,知识发现和数据挖掘应运而生.该文提出了数据挖掘的基本概念,数据挖掘是数据库技术、人工智能、机器学习、统计分析、模糊逻辑、模式识别和人工神经网络等多个学科相结合的产物,然后分析了数据挖掘一般算法的结构,并且对数据挖掘技术进行了详细分类,主要包括决策树技术、神经网络技术、粗集以及模糊集等十多项挖掘技术.最后讨论了数据挖掘在人工智能、电子商务应用和移动通信计算等方面的研究方向.     

Improving the Fuzzy Min-Max neural network with a K-nearest hyperbox expansion rule for pattern classification

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations of the original FMM network and to improve its classification performance is derived. In particular, the K-nearest hyperbox expansion rule is formulated to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox during the FMM learning stage. The effectiveness of the proposed model is evaluated using a number of benchmark data sets. The results compare favorably with those from various FMM variants and other existing classifiers.     

Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks

Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system. They have the ability of predicting, learning from experiences and generalizing from previous examples. An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights. Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs. This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors. Fuzzy DIFACONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes. Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets.     

Time sequence data mining using time–frequency analysis and soft computing techniques

This paper presents a new approach for time series data mining and knowledge discovery. The relevant features of non-stationary time series data from power network disturbances are extracted using a multiresolution S-transform which can be treated either as a phase corrected wavelet transform or a variable window short-time Fourier transform. After extracting the relevant features from the time series data, an integrated LVQ neural network and various feed-forward neural network architectures are used for pattern recognition of disturbance waveform data. The fuzzy MLP outperforms all the other different connectionist models and is used in the final stage for encoding knowledge in the connection weights that are used to generate rules for fuzzy inferencing of the disturbance patterns. Overall pattern classification accuracy of 99% is achieved for power signal time series data. The knowledge discovery from the data has then been presented for selected patterns using the new quantification procedures. The approach presented in this paper is a general one and can be applied to any time series data sequence for mining for similarities in the data.     

基于模糊高斯基函数神经网络的遥感图像分类

针对遥感图像分类的特点,提出了一种基于模糊高斯基函数神经网络的遥感图像分类器。该分类器将模糊技术与神经网络相结合,采用神经网络来实现模糊推理,利用神经网络的学习能力来达到调整模糊隶属函数和模型规则的目的,从而使系统具备了自适应的特性,实验结果表明,这种基于模糊高斯基孙数神经网络的分类器经过训练后,可应用于遥感图像的分类,其分类精度明显高于传统的最大似然分类法。     

Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction–repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if–then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.     

Performance evaluation of fuzzy classifier systems formultidimensional pattern classification problems

We examine the performance of a fuzzy genetics-based machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if-then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if-then rules and fuzzy reasoning for pattern classification problems. Then we explain a genetics-based machine learning method that automatically generates fuzzy if-then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if-then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if-then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some well-known test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.     

Fast connectionist learning: words and case

Some basic principles of connectionist research are explained along with an account of a number of the techniques necessary for constructing connectionist models. The objective is to introduce the area to people with limited mathematical and computational backgrounds by reducing the examples to simple arithmetic. In this way, a solid basis will be provided for one of the learning algorithms that have been fundamental to the development of network learning: the Hebbian learning rule. After outlining the technique in detail, two examples are provided to make the ideas concrete. These are learning to associate visual features with words and learning case representations.1. Of course, this is a very simple account of language representation, but it suffices our current purposes. We do not discuss more difficult problems such as prepositional attachment and recursion.Notes     

An adaptive neural fuzzy filter and its applications

A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically     

Reinforcement learning combined with a fuzzy adaptive learning control network (FALCON-R) for pattern classification

Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster, is able to escape from local minima, and has excellent disturbance rejection capability.     

Fuzzy multi-layer perceptron, inferencing and rule generation.

A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the more important input feature information, if and when required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The magnitudes of the connection weights of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the speech recognition problem, on some medical data and on artificially generated intractable (linearly nonseparable) pattern classes.     

A fuzzy neural network model and its hardware implementation

A fuzzy classifier based on a four-layered feedforward neural network model is proposed. This connectionist fuzzy classifier, called CFC, realizes the weighted-Euclidean-distance fuzzy classification concept in a massively parallel manner to recognize input patterns. CFC employs a hybrid supervised/unsupervised learning scheme to organize referenced pattern vectors. This scheme not only overcomes the major drawbacks of multilayer perceptron models using the backpropagation algorithm, i.e., the local minimal problem and long training time, but also avoids the disadvantage of the huge storage space requirement of the probabilistic neural network. According to experimental results, CFC shows better accuracy for speech recognition than several existing methods, even in a noisy environment. Moreover, it has higher stability of recognition rates for different environmental conditions. A massively parallel hardware architecture has been developed to implement CFC. A bus-oriented multiprocessor, systolic processor structure, and pipelining are used to obtain low-cost, high-performance fuzzy classification