Tsutomu Miki

Silicon Implementation for a Novel High-Speed Fuzzy Inference Engine: Mega-Flips Analog Fuzzy Processor

This article describes two types of analog fuzzy processors. One is a rule chip FP-9000 and the other a defuzzifier chip FP-9001, and both can achieve high-speed fuzzy inference. The inference speed is more than 1 Mega fuzzy logical inferences per second, excluding defuzzification. The rule chip includes four fuzzy inference engines. Each engine achieves one fuzzy inference, which is characterized by a fuzzy if-then rule, in analog mode. These rules can be directly read out and written in through an external digital computer. The rule chip is fabricated in 2 μm BiCMOS technology. The defuzzifier chip converts a fuzzy value to a crisp one, necessary for a fuzzy control system. The defuzzifier chip is implemented by 3 μm bipolar technology. A high-speed fuzzy controller hardware system can be efficiently constructed with these chips. The chips accelerate to develop fuzzy logic systems, especially high-speed applications.

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. >

Fuzzy rule-based simple interpolation algorithm for discrete signal

Abstract This paper describes a fuzzy rule-based simple interpolation algorithm for discrete data. A simple interpolation algorithm between two noise-free points in the Euclidean space is proposed by taking into account the fuzzy effects of the surrounding points. The advanced algorithm for the noisy points is proposed, where interpolation is done by classifying the neighboring noisy points into some groups. The validity and the effectiveness of these methods have been verified by computer simulations and by applications to the actual time series noisy data. The experimental results have provided substantial proofs for practical use.

An Effective Simple Shepherding Algorithm Suitable for Implementation to a Multi-Mmobile Robot System

In this paper, a simple and effective shepherding algorithm is presented. The shepherding is to guide or control a flocking behavior by one or more external agents which called shepherds. In general, a complex strategy is necessary for treating with a flock which has a lot of members. We propose a simple scheme using only simple rules like boids rule proposed by C. Raynolds. Behavior of the shepherd is derived from only simple rules. The validity of the proposed method is confirmed for demonstrations a flock with a lot of members which is herded by single to two shepherds and the limitations are discussed. The computer simulations show that the proposed method is possible to control a flock with about 25 members by single shepherd and about 30 members by two shepherds. Furthermore, the autonomous cooperation for two shepherds can be generated by the proposed rules

An FPGA-based CollisionWarning System Using Hybrid Approach

In this paper, we propose an FPGA-based collision warning system for advanced automobile driver assistance systems or autonomous moving robots. The system consists of three function blocks: coarse edge detection using a resistive-fuse network, moving-object detection inspired by neuronal propagation in the hippocampus, and danger evaluation and collision warning using fuzzy inference. The first two functions are implemented in FPGAs. The system can detect moving objects with a speed range of 3-192 km/h with a sampling period of 30 ms for an input image of 320 x 256 pixels, and can output a warning against dangerous regions in the input image.

Facial Feature Extraction based on a Degree of Perceptual Importance

Feature extraction is a key issue in the image processing and recognition. Especially, in the robot vision, intelligent methods such as human visual system are demanded. As a perceptually motivated image model, the three-component image model has been proposed, which decomposes components corresponding to degrees of the perceptual importance. We pay attention to its effective extraction ability of the perceptually significant region of the image and try to apply the algorithm to the feature extraction. However the approach consumes a lot of computing time because of its complex computing algorithm. In this paper, human-like feature extraction by using a nonlinear function network is proposed. This method realizes the feature extraction based on the three-component image model with a hardware friendly algorithm. The validity of the proposed method is confirmed with experimental results of facial feature extraction as compared with the standard Laplacian-Gaussian operator edges extraction scheme.

Cooperative behavior generation method using local communication for distributed multi-agent systems

An effective distributed multi-agent system using local communication is proposed in this paper, which employs a state-based approach and achieves a complex task without any centralized agent. In general, many researches on cooperative multi-agent system employ a centralized approach which uses a centralized agent as supervisor. However, it has drawbacks that it is weak for a failure of the centralized agent and the task management becomes difficult as the number of agents increases. We propose a distributed-type cooperative multi-agent system in which tasks of each agent are arranged effectively by using local information. In the proposed model, each agent determines effective actions by using a situation of a whole system estimated from a local communication between neighboring agents. The proposed system offers a low computing cost and effective cooperative actions. The performance of the proposed system is confirmed by computer simulations on surrounding task.

Hardware implementation of fuzzy filtering

The authors describe a simple interpolation algorithm for noisy signal data using fuzzy inference and its hardware implementation. Concretely, keeping in mind that even if the signal data are disturbed by noise, a rough sketch of the true signal pattern can be generally made, an interpolation algorithm based on fuzzy logic is proposed. It results eventually in the reduction of noise in the signal data with no knowledge of its dynamics. The effectiveness of the method was verified by computer simulations, and the method was implemented by a hybrid electronic circuit on a breadboard.<<ETX>>

A morphological associative memory employing a reverse recall

Recently the morphological associative memory proposed by Ritter attracts researchers attention. The model is superior to other models in terms of memory capacity and perfect recall rate. However the conventional MAM has a problem that the correct pattern cannot be recalled if a pattern has inclusive relation to other stored pattern. In this paper, as one of the solutions, an effective MAM employing a reverse recall is proposed. In the proposed method, candidate patterns of an input can be estimated by reverse recall from the kernel image recalled by a given inclusion input pattern, and then the plausible recall pattern can be determined by comparing the candidates with input pattern. We confirm the validity of the proposed method through hetero association experiments for twenty six alphabet patterns with inclusion patterns.

Effectiveness of Scale Free Network to the Performance Improvement of a Morphological Associative Memory without a Kernel Image

In this paper, we present a new approach of the morphological associative memory (MAM) without a kernel image to reduce the network size by using the scale free network. The MAM is one of the powerful associative memories compared to ordinary associative memories. Weak point of the MAM is to need the kernel image which is susceptibility to noise and hard to design. We have already presented the MAM without a kernel image as a practical model. However the model has a drawback that the perfect recall rate is degraded. On the other hand, it has been reported that an introduction of the scale free networkto associative memories is effective in the improvement of the recall rate and the reduction of the network size. We try to reduce the network size and improve the recall rate by introducing the scale free network.