Genetic Searching Algorithm for Optimal Runlength—Limited Codes with Error Control

ＧｅｎｅｔｉｃＳｅａｒｃｈｉｎｇＡｌｇｏｒｉｔｈｍｆｏｒＯｐｔｉｍａｌＲｕｎｌｅｎｇｔｈ┐ＬｉｍｉｔｅｄＣｏｄｅｓｗｉｔｈＥｒｒｏｒＣｏｎｔｒｏｌ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｈｉｎｅｓｅＫｅｙＰｒｏｊｅｃｔｏｆＦｕｎｄａｍ...     

Evolutionary Computation:an Overview

ＥｖｏｌｕｔｉｏｎａｒｙＣｏｍｐｕｔａｔｉｏｎ：ａｎＯｖｅｒｖｉｅｗ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｌｉｍｂｉｎｇＰｒｏｇｒａｍｍｅ┐ＮａｔｉｏｎａｌＫｅｙＰｒｏｊｅｃｔｆｏｒＦｕｎｄａ┐ｍｅｎｔａｌＲｅｓｅａｒｃｈｉｎＣｈｉ...     

A Homotopy-Controlled Mean Field Annealing Partially-Connected Neural Equalizer for Pan-European GSM System

ＡＨｏｍｏｔｏｐｙ┐ＣｏｎｔｒｏｌｅｄＭｅａｎＦｉｅｌｄＡｎｎｅａｌｉｎｇＰａｒｔｉａｌｙ┐ＣｏｎｎｅｃｔｅｄＮｅｕｒａｌＥｑｕａｌｉｚｅｒｆｏｒＰａｎ┐ＥｕｒｏｐｅａｎＧＳＭＳｙｓｔｅｍ＊Ｔｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｉｎｐａｒｔｂｙｔｈ...     

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

ＡｐｐｌｉｃａｔｉｏｎｏｆＲｅｃｕｒｒｅｎｔＷａｖｅｌｅｔＮｅｕｒａｌＮｅｔｗｏｒｋｓｔｏｔｈｅＤｉｇｉｔａｌＣｏｍｍｕｎｉｃａｔｉｏｎｓＣｈａｎｎｅｌＢｌｉｎｄＥｑｕａｌｉｚａｔｉｏｎ＊＊ＴｈｉｓｗｏｒｋｗａｓｓｕｐｐｏｒｔｅｄｂｙｔｈｅＣｌｉｍｂ...     

Fabrication of Rare Earth Doped Optical Fiber

ＦａｂｒｉｃａｔｉｏｎｏｆＲａｒｅＥａｒｔｈＤｏｐｅｄＯｐｔｉｃａｌＦｉｂｅｒＦａｂｒｉｃａｔｉｏｎｏｆＲａｒｅＥａｒｔｈＤｏｐｅｄＯｐｔｉｃａｌＦｉｂｅｒ￥ＺＨＵＹｉ－Ｎｉａｎ（ＳｈａｎｇｈａｉＥｌｅｃｔｒｉｃＣａｂｌｅＲｅｓｅａｒｃｈＩｎｓｔｉｔ...     

电信英语及其缩略语解释（十）

ＢＥＰ Ｂｌｏｃｋ Ｅｒｒｏｒ Ｐｒｏｂａｂｉｌｉｔｙ 码组差错概率ＢＥＲ Ｂｉｔ Ｅｒｒｏｒ Ｒａｔｉｏ 误码率 ＢＥＲ指传输中码元的错误概率。ＢＥＲＴ Ｂｉｔ Ｅｒｒｏｒ Ｒａｔｉｏ Ｔｅｓｔｅｒ 误码率监测器ＢＥＴＲＳ Ｂａｓｉｃ Ｅｘｃｈａｎｇｅ Ｔｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎｓ Ｒａｄｉｏ Ｓｅｒｖｉｃｅ 基本交换无线通信业务ＢＥＸ Ｂｒｏａｄｂａｎｄ ＥＸｃｈａｎｇｅ 宽带交换机ＢＥＸＲ Ｂａｓｉｃ ＥＸｃｈａｎｇｅ Ｒａｄｉｏ 基本交换无线系统ＢＦＩ Ｂａｄ Ｆｒａｍｅ Ｉｎｄｉｃａｔｉｏｎ 坏帧指示 ＢＦ…     

在Si衬底上生长高跨导p型Ge沟MODFET

在Ｓｉ衬底上生长高跨导ｐ型Ｇｅ沟ＭＯＤＦＥＴ＝ｐ－ＴｙｐｅＧｅ－ｃｈａｎｎｅｌＭＯＤＦＥＴ’ｓｗｉｔｈｈｉｇｈｔｒａｎｓｃｏｎｄｕｃｔａｎｃｅｇｒｏｗｎｏｎＳｉｓｕｂｓｔｒａｔｅｓ［刊，英］／Ｋｏｎｉｇ，Ｕ．…／／ＩＥＥＥＥｌｅｃｔｒｏｎＤｅｖｉｃｅ...     

ALCHEMI研究中的误差

ＡＬＣＨＥＭＩ研究中的误差陈清吴杏芳Ｃ．Ｊ．Ｈｕｍｐｈｒｅｙｓ＊（北京科技大学材料物理系，北京１０００８３）（＊英国剑桥大学材料系）ＡＬＣＨＥＭＩ（ＡｔｏｍＬｏｃａｔｉｏｎｂｙＣｈａｎｎｅｌｉｎｇ－ＥｎｈａｎｃｅｄＭｉｃｒｏａｎａｌｙｓｉｓ，原子位置...     

A Very Efficient Approach for the Synthesis of 2-D Recursive Fan Filters

ＡＶｅｒｙＥｆｆｉｃｉｅｎｔＡｐｐｒｏａｃｈｆｏｒｔｈｅＳｙｎｔｈｅｓｉｓｏｆ２－ＤＲｅｃｕｒｓｉｖｅＦａｎＦｉｌｔｅｒｓＺｈｕＷｅｉｐｉｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＴｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎＥｎｇｉｎｅｅｒｉｎｇ，ＮａｎｊｉｎｇＵｎｉｖｅ...     

The Application of Save Functions of SDL in Communication Systems

1　ＩｎｔｒｏｄｕｃｔｉｏｎＩｔｉｓａｃｏｍｐｌｅｘａｎｄｄｉｆｆｉｃｕｌｔｊｏｂｔｏｄｅｓｉｇｎｔｈｅｓｏｆｔｗａｒｅｏｆｍｏｄｅｒｎｃｏｍｍｕｎｉｃａｔｉｏｎｓｙｓｔｅｍｓ.ＴｈｅｓｏｆｔｗａｒｅｏｆｃｏｍｍｕｎｉｃａｔｉｏｎｓｙｓｔｅｍｓｉｓｃｏｍｐｏｓｅｄｏｆＥｘｔｅｎｄｅｄＦｉｎｉｔｓＳｔａｔｅＭａｃｈｉｎｅ (ＥＦＳＭ )ｔｈａｔｒｕｎｓｉｎｐａｒａｌｌｅｌ.Ｔｈｅｓｅｓｔａｔｅｍａｃｈｉｎｅｓａｒｅｉｎｄｅｐｅｎｄｅｎｔｏｆｅａｃｈｏｔｈｅｒａｎｄｃｏｍｍｕｎｉｃａｔｅｗｉｔｈｄｉｓｃｒ…     

基于模糊神经网络融合遗传算法的模拟电路故障诊断法

该文提出了一种非线性模拟系统的故障辨识方法，构造了一个基于模糊加权型推理法的模糊神经网络，利用遗传算法来训练网络连接权值、优化隶属度函数，根据训练后的网络权值可以自动提取出模糊规则，并通过仿真实验验证了该方法的有效性。     

An Optimum Vertical Handoff Decision Algorithm Based on Adaptive Fuzzy Logic and Genetic Algorithm

Handoff decision making is one of the most important topics in wireless heterogeneous networks architecture as there are many parameters which have to be considered when triggering handoff and selecting suitable access point. More intelligent approaches which reckon user profiles, application requirements, and network conditions must be improved so that desired performance results for both user and network could be provided. In this paper we introduce a new adaptive vertical handoff decision making algorithm in which fuzzy membership functions are optimized by means of genetic algorithm. Genetic algorithm is an adaptive search technique based on natural selection and genetic rules. In addition to that, it takes places in various scientific applications and can be used to adjust the membership functions in fuzzy systems. The purpose of the study is to adjust the shape of fuzzy membership functions, properly, using genetic algorithm in order to achieve optimum handoff performance. The results show that, compared to the several different algorithms performance of the proposed approach with genetic algorithm is significantly improved for both user and network in terms of number of handoff while the other requirements are still satisfied.     

神经网络自动生成模糊系统

在模糊系统的生成过程中，最主要的任务是隶属函数和模糊规则的提取和调整，但用传统方法，其工作量往往随变量数的增长而爆炸性地增加。为了解决这一问题，本文提出了一种新颖的，利用神经网络来自动地撮模糊系统 隶属函数和规则。     

A neural fuzzy inference system

This paper proposes a new neural fuzzy inference system that mainly consists of four parts. The first part is about how to use neural network to express the relation within a fuzzy rule. The second part is the simplification of the first part, and experiments show that these simplifications work. On the contrary to the second part, the third part is the enhancement of the first part and it can be used when the first part cannot work very well in the fuzzy inference algorithm, which would be introduced in the fourth part. Finally, the fourth part "neural fuzzy inference algorithm" is been introduced. It can inference the new membership function of the output based on previous fuzzy rules. The accuracy of the fuzzy inference algorithm is dependent on neural network generalization ability. Even if the generalization ability of the neural network we used is good, we still get inaccurate results since the new coming rule may not be related to any of the previous rules. Experiments show this algorithm is successful in situations which satisfy these conditions.     

A recurrent fuzzy cellular neural network system with automatic structure and template learning

It is widely accepted that using a set of cellular neural networks (CNNs) in parallel can achieve higher level information processing and reasoning functions either from application or biologics points of views. Such an integrated CNN system can solve more complex intelligent problems. In this paper, we propose a novel framework for automatically constructing a multiple-CNN integrated neural system in the form of a recurrent fuzzy neural network. This system, called recurrent fuzzy CNN (RFCNN), can automatically learn its proper network structure and parameters simultaneously. The structure learning includes the fuzzy division of the problem domain and the creation of fuzzy rules and CNNs. The parameter learning includes the tuning of fuzzy membership functions and CNN templates. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. A new online adaptive independent component analysis mixture-model technique is proposed for the structure learning of RFCNN, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. The proposed RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the proposed RFCNN is demonstrated on the real-world defect inspection problems. Experimental results show that the proposed scheme is effective and promising.     

基于模糊预测控制的船舶动力定位系统的设计

在系统分析和研究船舶动力定位系统的基础上,提出一种基于模糊预测控制的船舶动力定位的方法,设计了模糊预测控制器。首先,在预测控制部分,探讨预测模型和反馈校正的设计;在模糊控制部分,研究隶属函数和模糊规则的具体制定。最后采用工程数据,对该算法和模糊预测控制器进行了仿真验证和性能评判,结果表明,所设计的控制器能对船舶进行有效的定位。     

On the Equivalence of a Table Lookup (TL) Technique and Fuzzy Neural Network (FNN) With Block Pulse Membership Functions (BPMFs) and Its Application to Water Injection Control of an Automobile

This paper presents an alternative method to design a fuzzy neural network (FNN) using a set of nonoverlapped block pulse membership functions (BMPFs), and this FNN with nonoverlapped BPMFs will be shown to be equivalent to the conventional table lookup (TL) technique. Therefore, the hidden links between TL and FNN techniques are revealed in this paper that provides a methodology to design a TL controller based on the FNN design concept. In order to do so, a new direct formula is first developed to generate the fuzzy rules from the premise part in FNN. This direct formula not only guarantees a one-to-one mapping that maps the fuzzy membership functions onto the fuzzy rules, but also alleviates the coding effort during hardware implementation. It is further elaborated that the FNN with nonoverlapped BPMFs has the advantage of faster online training that requires less computation time, but at the cost of more memory requirement to store the fuzzy rules. The application of this new approach has been applied successfully in the water injection control of a turbo-charged automobile with excellent results.     

ADS-B系统运行可靠性评价方法

为实现ADS-B系统运行可靠性的综合评价,提出了一种基于T-S模糊神经网络的方法。以平均首次故障时间、平均故障间隔时间、平均维修时间、平均可用度和维修时间率5项可靠性评价指标作为输入向量。利用T-S模糊系统构建可靠性综合评价的非线性映射关系,经神经网络的自适应训练,调整模糊规则和隶属度函数,建立ADS-B系统运行可靠性的综合评价模型。实验仿真与应用情况表明,该方法可行、有效,并具有较高的计算稳定性、精确性和良好的泛化能力,且评价结果客观、准确可靠,可作为提高ADS-B系统运行可靠性的决策依据。     

Current-Mode Fuzzy Linguistic Hedge Circuits

In this paper, a current-mode design methodology to implement a set of fuzzy linguistic hedge circuits is proposed. The so-called fuzzy linguistic hedge is a fuzzy operation applied to adjust the membership function of a fuzzy set. The fuzzy membership function of control variable and the control rules are very important in a fuzzy logic controller because they dominate the control strategies. If the control results fail to meet the system requirements, the control objective can still be achieved by adjusting the membership function of the fuzzy set or the control rules. Moreover, the adjustment effect of the control strategies through the modifications of the fuzzy membership function is the same as that of the system control rules. In this paper, we propose a set of fuzzy linguistic hedge circuits, including absolutely, very, much more, more, plus minus, more or less, slightly, and contrast intensification, which has been fabricated in 0.8 m CMOS process. Experimental results show that the average error of the circuits is within 1% of the full scale current. Under the power supply voltage of 3.3 V, the operating dynamic range is 50 A. Furthermore, these circuits still work well even when the power supply voltage is down to 2.5 V. In addition, in real world application, we can incorporate a membership function generator, a fuzzification unit, a multi-input maximum/minimum circuit, and a defuzzification unit with the linguistic hedge used to modify the membership function in order to develop a real-time adaptive fuzzy logic controller.     

一种神经网络模糊控制器的合成

论述了一种神经网络模糊控制器的合成方法，提出了单元-单元映象方法及利用模糊神经网络确定模糊中心，识别模型规则，调节隶属函数参数。仿真试验表明该模糊控制器相比PID控制器具有较好的性能。