基于模糊神经网络系统的智能小车避障

智能小车把超声波传感器和红外传感器相结合来感知外界环境的信息,并按照一定的规则来调整小车的方位角和速度,实现智能小车的自主导航和避障。模糊神经网络作为人工智能的分支,兼具模糊逻辑系统和神经网络各自的优点,具有表达和处理确定的信息、模糊信息的能力和良好的学习能力等特点。把模糊逻辑系统和神经网络结合起来,运用到智能小车避障的自适应控制中,并且使用一种多层前馈型神经网络即BP神经网络在模糊神经系统中解决神经网络的权系数优化问题。     

Synergistic modeling and applications of hierarchical fuzzy neuralnetworks

Many common foundations exist between neural networks and fuzzy inference systems in terms of their mathematical models and system structures. This paper explores such a rich synergy and uses it to form the basis for a unifying framework under which fuzzy logic processing and neural networks may be integrated to achieve more robust information processing. It in turn leads to a family of hierarchical fuzzy neural networks (FNNs) which incorporate an adaptive and modular design of neural networks into the basic fuzzy logic systems. Several important models which are critical to the development of the the hierarchical FNN family are studied. We demonstrate how existing unsupervised and supervised learning strategies can be an integral part of a fuzzy processing framework. In addition, hierarchical structures involving both expert modules and class modules are incorporated into the FNNs. Also presented are some promising application examples     

A QoS-provisioning neural fuzzy connection admission controller formultimedia high-speed networks

This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract     

模糊自适应估计器在INS/GPS组合导航中的应用研究

研究了一种模糊自适应神经网络状态估计器的工作原理及结构,并讨论了用其代替传统扩展卡尔曼滤波在导航参数估计中的实现方法。利用扩展卡尔曼滤波来构造模糊推理系统,通过神经网络对模糊推理系统进行训练,根据输入输出样本自动调整模糊系统的设计参数,从而实现对INS/GPS组合导航系统导航参数的最优估计。通过仿真验证表明,用模糊自适应状态估计方法进行导航系统的初始对准,能够大大提高系统的实时性。     

Neurocontrol and elastic fuzzy logic: capabilities, concepts, andapplications

The author shows how elastic fuzzy logic (EFL) nets make it possible to combine the capabilities of expert systems with the learning capabilities of neural networks at a high level. ANN (artificial neural network) implementations have advantages in terms of hardware implementation, ease of use, generality, and links to the brain, which is still the only true intelligent controller available. Neurocontrol is useful in cloning experts, tracking trajectories or setpoints, and optimization (e.g., approximate dynamic programming). There has been substantial success in controlling robot arms (including the main arm of the Space Shuttle), chemical process control, continuous production of high-quality parts, and other aerospace applications. A review of the basic designs and concepts, with reference to both the applications and future research opportunities, is given     

PROMS: a PRO‐active Monitoring System for SS7 networks

In this paper, we propose a PRO‐active Monitoring System (PROMS) for SS7 networks, which actively monitors all signaling network management messages of SS7 networks, alerts operators when there is a potential network error, and provides intelligent diagnosis based on fuzzy logic and neural networks. Copyright © 2000 John Wiley & Sons, Ltd.     

神经网络在ATM光纤高速智能管理网中的应用(连载)

首先扼要地讨论了光纤通信领域在新世纪面临的严重任务 ,接着详细地介绍了神经网络的基本概念 ,重点论述了神经网络在 ATM光纤高速智能管理网中的应用。最后指出 ,神经网络应用于通信领域可以实现快捷、灵活、自适应性和智能化实时管理 ,在某些方面可以解决即使数学计算机也会感到棘手的难题。     

基于模糊神经网络智能预测模型的设计与实现

针对智能决策支持系统中经常遇到的预测类问题,根据人工神经网络和模糊逻辑系统的各自特点,设计一种模糊神经网络模型,将模糊系统用类似于神经网络的结构表示,再用相应的学习算法训练模糊系统实现模糊推理.并对此模型进行预测验证和编程实现.     

Intelligent control of complex electrochemical systems with aneuro-fuzzy-genetic approach

This paper describes different hybrid approaches for controlling the battery charging process. The hybrid approaches combine soft computing techniques to achieve the goal of controlling the temperature of the battery during the electrochemical charging process. We have reduced the time required for charging a battery with the use of fuzzy logic, neural networks, and genetic algorithms. In the neuro-fuzzy-genetic approach, neural networks are used for modeling the electrochemical process, fuzzy logic is used for controlling the process, and genetic algorithms are used to optimize the fuzzy system     

热印字技术

热印字机是近年发展起来的击打式印字机。文中主要介绍热印字机的分类，三种热印字技术，热印字机关键技术，并介绍目前常见的机型及应用情况。     

Intelligent traffic control for ATM broadband networks

Performance results prove that a neural networks approach achieves better results, simpler and faster, than algorithmic approaches. The focus of this paper is to shed light on how neural networks (NNs) can be used to solve many of the serious problems encountered in the development of a coherent traffic control strategy in ATM networks. The main philosophy that favors neural networks over conventional programming approaches is their learning and adaptive capabilities, which can be utilized to construct adaptive (and computationally intelligent) algorithms for allocation of resources (e.g., bandwidth, buffers), thus providing highly effective tools for congestion control     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

一种新型的船舶模糊控制方法

针对常规船舶模糊控制存在的缺陷,提出一种基于线性插值的船舶模糊控制方法,该方法可以在线计算出系统的控制输出,通过使控制规则表内相邻分档之间补充了无穷多个经过细分的控制规则,克服由于量化误差而引起的稳态误差和稳态颤振现象。以本校远洋实习船“育龙轮”为例,利用Matlab的Simulink工具进行了仿真研究,对比常规模糊控制,此种方法具有良好的控制性能。     

基于神经网络的模糊呼叫接纳控制器

本文提出了一种新的基于神经网络的模糊逻辑技术实现呼叫接纲控制（ＣＡＣ）的方法,与传统的神经网络方法相比,该方法利用了两种技术的优点,能合理地选择初始参数,同时具有自学习能力,模拟结果表明,我们提出的方法能够利用较少的训练数据获得较高的精确怀,减少了训练时间,体现了传统才神经网络方法不具备的优越性。     

适应型模糊神经网络在视觉辩识系统中的应用

本文构筑的适应型模糊神经网络模型实现了神经网络的学习训练能力、模糊逻辑系统的仿人推理功能以及匹配寻踪的适应性技术的结合。以其对具有不确定性特征的机器视觉目标图像进行辨识处理,取得良好效果。     

Fuzzy logic systems for engineering: a tutorial

A fuzzy logic system (FLS) is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data (feature) vector into a scalar output, i.e., it maps numbers into numbers. Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear mapping. This tutorial paper provides a guided tour through those aspects of fuzzy sets and fuzzy logic that are necessary to synthesize an FLS. It does this by starting with crisp set theory and dual logic and demonstrating how both can be extended to their fuzzy counterparts. Because engineering systems are, for the most part, causal, we impose causality as a constraint on the development of the FLS. After synthesizing a FLS, we demonstrate that it can be expressed mathematically as a linear combination of fuzzy basis functions, and is a nonlinear universal function approximator, a property that it shares with feedforward neural networks. The fuzzy basis function expansion is very powerful because its basis functions can be derived from either numerical data or linguistic knowledge, both of which can be cast into the forms of IF-THEN rules     

Fuzzy neural control of voice cells in ATM networks

This paper presents the design of a fuzzy controller for managing cells generated by voice sources in asynchronous transfer mode (ATM) networks. Typical voice cells, characterized by a high degree of burstiness, complicate any attempt to use classical control theory in the design of an ATM cell rate controller. The fuzzy control approach presented in this paper overcomes this limitation by appealing to the linguistic ability of fuzzy set theory and logic to handle the complexity. Specifically, the cell rate control problem is linguistically stated but treated mathematically via fuzzy set manipulation. In particular, the ATM voice cell controller being proposed is an improved and intelligent implementation of the leaky bucket cell rate control mechanism extensively studied in the literature. This intelligent implementation of the leaky bucket mechanism uses a channel utilization feedback via the QoS parameters to improve its performance. This ATM fuzzy controller takes the form of an organized set of linguistic rules quantitatively expressed and manipulated by means of fuzzy set theory and fuzzy logic. The fuzzy control rules are stored in fuzzy associative memory to permit parallel executions     

Temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance     

Adaptive fuzzy systems for multichannel signal processing

Processing multichannel signals using digital signal processing techniques has received increased attention lately due to its importance in applications such as multimedia technologies and telecommunications. The objective of this paper is twofold: 1) to introduce adaptive filtering techniques to the reader who is just beginning in this area and 2) to provide a review for the reader who may be well versed in signal processing. The perspective of the topic offered here is one that comes primarily from work done in the field of multichannel (color) image processing. Hence, many of the techniques and works cited here relate to image processing with the emphasis placed primarily on filtering algorithms based on fuzzy concepts, multidimensional scaling, and order statistics-based designs. It should be noted, however, that multichannel signal processing is a very broad field and thus contains many other approaches that have been developed from different perspectives, such as transform domain filtering, classical least-square approaches, neural networks, and stochastic methods, just to name a few. We present a general formulation based on fuzzy concepts, which allows the use of adaptive weights in the filtering structure, and we discuss different filter designs. The strong potential of fuzzy adaptive filters for multichannel signal applications, such as color image processing, is illustrated with several examples     

Achieving of Fuzzy Automata for Processing Fuzzy Logic

At present, there has been an increasing interest in neuron-fuzzy systems, the combinations of artificial neural networks with fuzzy logic. In this paper, a definition of fuzzy finite state automata （FFA） is introduced and fuzzy knowledge equivalence representations between neural networks, fuzzy systems and models of automata are discussed. Once the network has been trained, we develop a method to extract a representation of the FFA encoded in the recurrent neural network that recognizes the training rules.