A general-purpose fuzzy inference processor

The SAE 81C99 processor exhibits 4 different operation modes, 8 programmable fuzzy algorithms, and up to 256 inputs, 64 outputs, and 16,384 rules. The 1.0-μm CMOS chip serves as a stand-alone device or as an on-chip module for 8- or 16-bit microcontrollers. At 20-MHz crystal frequency and a maximum inference speed of 10 million rules/s, it supports very complex systems and millisecond (and faster) processes such as automotive electronics and pattern recognition     

Fuzzy classifications using fuzzy inference networks

In this paper, fuzzy inference models for pattern classifications have been developed and fuzzy inference networks based on these models are proposed. Most of the existing fuzzy rule-based systems have difficulties in deriving inference rules and membership functions directly from training data. Rules and membership functions are obtained from experts. Some approaches use backpropagation (BP) type learning algorithms to learn the parameters of membership functions from training data. However, BP algorithms take a long time to converge and they require an advanced setting of the number of inference rules. The work to determine the number of inference rules demands lots of experiences from the designer. In this paper, self-organizing learning algorithms are proposed for the fuzzy inference networks. In the proposed learning algorithms, the number of inference rules and the membership functions in the inference rules will be automatically determined during the training procedure. The learning speed is fast. The proposed fuzzy inference network (FIN) classifiers possess both the structure and the learning ability of neural networks, and the fuzzy classification ability of fuzzy algorithms. Simulation results on fuzzy classification of two-dimensional data are presented and compared with those of the fuzzy ARTMAP. The proposed fuzzy inference networks perform better than the fuzzy ARTMAP and need less training samples.     

A VLSI fuzzy inference processor based on a discrete analogapproach

In this paper we present a design for a general-purpose fuzzy processor, the core of which is based on an analog-numerical approach combining the inherent advantages of analog and digital implementations, above all as regards noise margins. The architectural model proposed was chosen in such a way as to obtain a processor capable of working with a considerable degree of parallelism. The internal structure of the processor is organized as a cascade of pipeline stages which perform parallel execution of the processes into which each inference can be decomposed. A particular feature of the project is the definition of a `fuzzy-gate', which executes elementary fuzzy computations, on which construction of the whole core of the processor is based. Designed using CMOS technology, the core can be integrated into a single chip and can easily be extended. The performance obtainable, in the order of 50 Mega fuzzy rules per second, is of a considerable level     

Fuzzy inference on an analog fuzzy chip

Our analog fuzzy processor features an inference speed of more than 1 million fuzzy logical inferences per second, excluding defuzzification. A rule chip processes fuzzy inferences while a second chip handles defuzzification, a functional division that facilitates flexible system configuration. The chips are compact fuzzy systems that save chip area and are suitable for built-in applications. They process high-speed fuzzy logic operations in parallel mode and, during execution of fuzzy inferences, feature an adaptable fuzzy system based on a rule set     

基于Sugeno型模糊推理算法的模糊控制器及其应用

介绍了Sugeno型模糊推理算法的基本原理,给出了一种实现方法,并对其控制性能进行了仿真.     

Fuzzy relation equations and fuzzy inference systems: an insideapproach

This paper investigates and extends the use of fuzzy relation equations for the representation and study of fuzzy inference systems. Using the generalized sup-t (t is a triangular norm) composition of fuzzy relations and the study of sup-t fuzzy relation equations, interesting results are provided concerning the completeness and the theoretical soundness of the representation, as well as the ability to mathematically formulate and satisfy application-oriented design demands. Furthermore, giving a formal study of fuzzy partitions and some useful aspects of fuzzy associations and fuzzy systems, the paper can be used as a theoretical background for designing consistent fuzzy inference systems.     

直觉F推理与普通F推理的比较研究

直觉F推理克服了普通F推理在不确定性信息的描述、推理结果可信性等方面存在的局限性。在介绍普通F推理直觉化扩展的基础上,首先分析了两类推理算法的相互转化问题,指出普通F推理是直觉F推理的一种特例,当直觉指数为0时二者可相互转化。其次,比较了两类算法的还原性,分析表明Zadeh型、Mamdani型、Larsen型直觉F推理算法与其对应的普通F推理算法具有相同的还原性。最后,通过实例研究了直觉F推理算法在推理结果精度、可信性上的优势,从而较普通F推理更适用于智能控制与决策。     

A fuzzy RISC processor

We describe application-specific extensions for fuzzy processing to a general purpose processor. The application-specific instruction set extensions were defined and evaluated using hardware/software codesign techniques. Based on this approach, we have extended the MIPS instruction set architecture with only a few new instructions to significantly speed up fuzzy computation with no increase of the processor cycle time and with only minor increase in chip area. The processor is implemented using a reconfigurable processor core which was designed as a starting point for application-specific processor designs to be used in embedded applications. Performance is presented for three representative applications of varying complexity     

Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization

In this paper, we introduce a new topology and offer a comprehensive design methodology of fuzzy set-based neural networks (FsNNs). The proposed architecture of the FsNNs is based on the fuzzy polynomial neurons formed through a collection of ‘if-then’ fuzzy rules, fuzzy inference, and polynomials with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. Three different forms of regression polynomials (namely constant, linear, and quadratic) are used in the consequence part of the rules. In order to build an optimal FsNN, the underlying structural and parametric optimization is supported by a dynamic search-based genetic algorithm (GA), which forms an optimal solution through successive adjustments (refinements) of the search range. The structure optimization involves the determination of the input variables included in the premise part and the order of the polynomial forming the consequence part of the rules. In the study, we explore two types of optimization methodologies, namely a simultaneous tuning and a separate tuning. GAs are global optimizers; however, when being used in their generic version, they often lead to a significant computing overhead caused by the need to explore an excessively large search space. To eliminate this shortcoming and increase the effectiveness of the optimization itself, we introduce a dynamic search-based GA that results in a rapid convergence while narrowing down the search to a limited region of the search space. We exploit this optimization mechanism to be completed both at the structural as well as the parametric level. To evaluate the performance of the proposed FsNN, we offer a suite of several representative numerical examples.     

A fast digital fuzzy processor

This digital fuzzy processor-designed and realized in 0.7-μm CMOS technology-demonstrates a processing rate from 80 to 320 ns. A parallel pipeline architecture supports fast selection of the active fuzzy rules. Specifically, we designed an Active-Rule-Selector for selecting a subset of the fuzzy rules, called active fuzzy rules, and divided the architecture into parallel and pipeline stages. Despite some initial difficulty, step by step our efforts yielded ever more feasible solutions. The foundry delivered the chip in 1997. So far, it works properly     

Adaptive-tree-structure-based fuzzy inference system

A new fuzzy inference system named adaptive-tree-structure-based fuzzy inference system (ATSFIS) is proposed, which is abbreviated as fuzzy tree (FT). The fuzzy partition of input data set and the membership function of every subset are obtained by means of the fuzzy binary tree structure based algorithm. Two structures of FT, FT-I, and FT-II, are presented. The characteristics of FT are: 1) The parameters of antecedent and consequent for a Takagi-Sugeno fuzzy model are learned simultaneously; and 2) The fuzzy partition of input data set is adaptive to the pattern of data distribution to optimize the number of the subsets automatically. The main advantage of FT is more suitable to solve the problems, for which the number of input dimension is large, since by using the fuzzy binary tree, every farther set will be partitioned into only two subsets no matter how large the input dimension is. Therefore, in some sense the "rule explosion" will be avoided possibly. In comparison with some existing fuzzy inference systems, it is shown that the FT is also of less computation and high accuracy. The advantages of FT are illustrated by simulation results.     

Robustness of interval-valued fuzzy inference

Since interval-valued fuzzy set intuitively addresses not only vagueness (lack of sharp class boundaries) but also a feature of uncertainty (lack of information), interval-valued fuzzy reasoning plays a vital role in intelligent systems including fuzzy control, classification, expert systems, and so on. To utilize interval-valued fuzzy inference better, it is very important to study the fundamental properties of interval-valued fuzzy inference such as robustness. In this paper, we first discuss the robustness of interval-valued fuzzy connectives. And then investigate the robustness of interval-valued fuzzy reasoning in terms of the sensitivity of interval-valued fuzzy connectives and maximum perturbation of interval-valued fuzzy sets. These results reveal that the robustness of interval-valued fuzzy reasoning is directly linked to the selection of interval-valued fuzzy connectives.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

Sparse distributed fuzzy inference systems

The sparse distributed architecture described would be shown to function effectively as a fuzzy inference system giving essentially the same results as conventional techniques. However, whereas the conventional model reaches a glass ceiling as the order of target systems increases due to computer architectural limitations, this design is able to surpass this limit. It uses the same principles of max–min composition to solve inference problems, and comprises fuzzy sets that can encode a level of linguistic expression typical of such systems. It however expresses fuzzy sets differently, and performs the required computation in a manner suitable to the alternative representation. It may seem a rather complicated solution for low order problems (which it is) with the situation reversing itself for high order problems, the conventional solution being complicated if not impossible and the new architecture simple. The limitation, errors and performance of the new method when compared to the conventional method is documented and quantified by software written to model the two representations considered.     

Fuzzy inference systems and their applications

The paper addresses the construction of fuzzy systems of the specification of processes of diagnosis and treatments of fuzzy inference, as well as new formulations and methods for solving problems in fuzzy models. In particular, approaches to the solution of classical probabilistic problems for fuzzy events are proposed.     

Genetic tuning of fuzzy inference within fuzzy classifier systems

Abstract: In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.     

Parallel exact inference on the Cell Broadband Engine processor

We present the design and implementation of a parallel exact inference algorithm on the Cell Broadband Engine (Cell BE) processor, a heterogeneous multicore architecture. Exact inference is a key problem in exploring probabilistic graphical models, where the computation complexity increases dramatically with the network structure and clique size. In this paper, we exploit parallelism in exact inference at multiple levels. We propose a rerooting method to minimize the critical path for exact inference, and an efficient scheduler to dynamically allocate SPEs. In addition, we explore potential table representation and layout to optimize DMA transfer between local store and main memory. We implemented the proposed method and conducted experiments on the Cell BE processor in the IBM QS20 Blade. We achieved speedup up to 10 × on the Cell, compared to state-of-the-art processors. The methodology proposed in this paper can be used for online scheduling of directed acyclic graph (DAG) structured computations.     

Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.     

A context switchable fuzzy inference chip

This paper describes a novel design of a fuzzy inference chip that allows for real-time online context switching. A context refers to a situation or scenario of an application requiring specific domain knowledge. In particular, our focus is on the class of applications involving embedded fuzzy control. The domain knowledge therefore refers to fuzzy rules and memberships. The kind of applications being considered is real-time in nature, which necessitates the implementation of hardware for fuzzy inferencing. The chip architecture is described and details on the design of the chip is presented.