基于空间转换网络的肺结节分类方法

为了降低肺结节分类问题中因样本的形变、扭曲、偏移等对分类结果的影响，提出一种基于空间转换网络的特征提取网络模型。在该模型中，利用空间转换网络的自适应仿射变换能力，赋予分类网络以自主矫正样本的能力，提升分类模型的鲁棒性。经过实验证明，该方法能够有效提高分类任务的准确率。     

网络加密算法的应用研究

本文从理论上对网络加密算法进行研究，对加密算法的特点进行了详细的分析，为网络中公文传输的问题提供解决办法。     

提高前馈神经网络推广能力的若干实际方法

提高前馈神经网络的推广能力是深受关注的问题，本文根据我们最近提出了一个网络有效推广的准则，从提高网络特征提取能力，分类能力和修改神经元激活函数等几方面给出了若干实际方案，我们在任意手写数字识别问题上的实验结果证实了这些方法的有效性。     

一种基于模糊分类网络的卷积码解码方法

本文提出一种基于模糊神经分类网络的卷积码译码方法，把译码工作转换为网络的分类工作。网络按照卷积码的编码方式由聚类自动生成，并且在网络中使用逻辑算子，因而网络的训练速度非常快，只需一次或几次迭代。而且对每个隐节点均定义了模糊隶属度函数，借以增加网络的联想能力，从而提高网络的纠错能力。在小约束度情况下，我们测试了该算法的性能，并与Ｖｉｔｅｒｂｉ算法的性能进行了比较。     

干扰条件下地域通信网链路阻塞率研究

链路是通信网络的重要组成部分。在干扰条件下,从理论上建立链路模型,并根据链路的分类和信道的误码率对链路阻塞率进行了研究。在此基础上,文章给出了某地域通信网一个四节点网络干扰时的链路选择示例,并进行了仿真。     

基于对向传播神经网络的舰船辐射噪声分类

针对舰船目标的分类识别,研究了从解调制(DEMON)谱的线谱和连续线谱中提取特征向量的方法,提出了1种改进的对向传播网络(MCPN)分类模型及算法。通过对海上实录的3类水中目标辐射噪声进行分类识别实验,实验结果证明:MCPN的分类能力及对未训练目标的适应性优于传统的对向传播网络(CPN)和误差反向传播网络(BPN)。     

一种简单的T型匹配网络的设计

根据阻抗匹配的特点，提出了一种简单易行的Ｔ型匹配网络，并从理论上给出了网络中所需各电感电容的计算公式，通过实际对几个换能器的匹配，发现实验结果与理论基本符合。     

基于信息扩散原理的RBF网络的分析与设计

从信息扩散的角度对ＲＢＦ网络进行了分析，从理论上证明了ＲＢＦ网络具有信息扩散功能，并分析了其网络的物理意义。说明了根据正态扩散的择近原则确定ＲＢＦ网络中规划因子的合理性，也说明了在训练样本数目大时用聚类方法确定中心参数的优越性。     

基于Kohonen神经网络的飞机目标识别

首先分析了Ｋｏｈｏｎｅｎ神经网络算法及其识别机理，结合飞机目标识别实现情况，借鉴人及从粗分到细分的思想，提出了基于ＫＮＮ－ＭＬＦＮＮ网络组分类器的飞机目标分类方法，应用于五种飞机目标的识别结果表明，自组织神经网络的学习速度快，自学习能力强；ＫＮＮ－ＭＬＦＮＮ网络组分类器有高的分类精度。     

一种简易的T型匹配网络的设计

根据阻抗匹配的特点，提出了一种简单易行的Ｔ型匹配网络，并从理论上给出了网络中所需各电感电容的计算公式，通过实际对几个换能器的匹配，发现实验结果与理论基本符合     

A supervisory fuzzy neural network controller for slider-crank mechanism

A supervisory fuzzy neural network (FNN) controller is proposed to control a nonlinear slider-crank mechanism in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive coupled with a slider-crank mechanism and a supervisory FNN position controller. The supervisory FNN controller comprises a sliding mode FNN controller and a supervisory controller. The sliding mode FNN controller combines the advantages of the sliding mode control with robust characteristics and the FNN with on-line learning ability. The supervisory controller is designed to stabilize the system states around a defined bound region. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results are provided to show that the proposed control system is robust with regard to plant parameter variations and external load disturbance.     

基于模糊神经网络的网络业务量预测研究

利用神经网络(NN)的自学习能力以及模糊逻辑的动态性和及时性等特点,将模糊逻辑和 NN 有机地结合起来,构造出了五层模糊神经网络(FNN),并用训练 NN 的相应学习算法-BP 算法来训练网络。本文将 FNN 用于网络自相似业务预测研究中,并与单纯的 NN 算法相比较。仿真结果表明,FNN 能很好地预测复杂网络业务,与传统的 NN 算法相比,不仅收敛速度快,且得到更好的预测效果。本文为复杂网络业务流量预测研究提供了一种有效途径。     

基于累积量的两层前馈神经网络盲辨识

由于非线性系统输出是其参数的非线性函数,直接利用高阶累积量辨识两层前馈神经网络(FNN)通常是十分困难的。为解决这一问题,该文提出两种基于四阶累积量的FNN辨识方法。第一种方法,FNN的隐元在其输入空间利用多个线性系统近似,进而FNN利用一统计模型混合专家(ME)网络重新描述。基于ME模型,FNN参数可利用统计期望值最大化(EM)算法获得估计。第二种方法,为简化FNN的ME模型,引入隐含观测量。基于隐含观测量估计,FNN被分解为多个单隐元的训练问题,进而整体FNN可利用一两阶层ME描述。基于单隐元的参数估计,FNN可利用一具有更快收敛速度的简化算法获得估计。     

基寸S7-200PLC的模糊神经网络算法设计

随着智能信息技术的发展,模糊神经网络算法广泛应用于工业控制。但该算法尚未应用于PLC。针对这种现状．给出基于S7-200PLC的模糊神经网络算法设计。利用模糊神经网络算法的理论知识,在S7—200的平台上采用梯形图和指令表两种模式编程设置。并利用PLC仿真软件对其仿真,仿真结果达到预期目标。     

故障诊断交流字典法的前向神经网络实现方法

提出了一种采用ＢＰ算法的前向多层神经网络实现模拟电路故障诊断交流字典的方法。     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

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.     

CDMA蜂窝系统中基于模糊神经网络的反向链路功率控制

CDMA是一个干扰受限系统,反向链路功率控制对于克服“远近效应”和增加系统容量是非常重要的.本文提出了一种基于模糊神经网络(FNN)的自适应闭环功率控制算法,该算法动态地调整功率控制增量,使基站接收到的每个用户的发射功率相等.仿真结果表明,由于模糊神经网络能够较好地识别反向链路的时变特性,FNN功率控制算法比传统的固定步长功率控制方法取得了更好的控制性能和更大的系统容量.而且,FNN能够通过神经网络训练自动地调整隶属度函数和模糊规则,从而适合于实现在线系统识别和自适应控制.     

Sliding-mode-controlled slider-crank mechanism with fuzzy neuralnetwork

The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which a FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller     

Comparison of sliding-mode and fuzzy neural network control formotor-toggle servomechanism

A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering