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外文翻译---通用回归神经网络在声呐目标分类中的应用.doc

仿生机器人的机构设计与运动仿真【开题+翻译+答辩稿+文献综述】【带PROE三维】【34张图纸】【优秀】

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仿生机器人的机构设计与运动仿真【开题+翻译+答辩稿+文献综述】【带PROE三维】【34张图纸】【优秀】.rar

外文翻译---通用回归神经网络在声呐目标分类中的应用.doc---(点击预览)

仿生机器人的机构设计与运动仿真说明书.doc---(点击预览)

仿生机器人的机构设计与运动仿真论文答辩稿.ppt---(点击预览)

仿生机器人的机构设计与运动仿真开题报告.doc---(点击预览)

wing032-qvzhou.dwg---(点击预览)

wing031-齿轮.dwg---(点击预览)

wing030-jiaozhang.dwg---(点击预览)

wing029-杆.dwg---(点击预览)

wing028-轴.dwg---(点击预览)

wing027-轴套.dwg---(点击预览)

wing026-板链接块.dwg---(点击预览)

wing025-键-销.dwg---(点击预览)

wing023-轴卡.dwg---(点击预览)

wing022-封板.dwg---(点击预览)

wing021-齿套.dwg---(点击预览)

wing020-zhengfang-shuangtou.dwg---(点击预览)

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wing018-销钉.dwg---(点击预览)

wing017-ka.dwg---(点击预览)

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wing015-bianzhengfang.dwg---(点击预览)

wing014-liangshuangtou-15.dwg---(点击预览)

wing013-liang-60.dwg---(点击预览)

wing012-zhengfang-cao.dwg---(点击预览)

wing011-suo.dwg---(点击预览)

wing010-杆.dwg---(点击预览)

wing009-zhuanjiao.dwg---(点击预览)

wing008-zhengfang-kong.dwg---(点击预览)

wing007-liang-15.dwg---(点击预览)

wing006-接头销.dwg---(点击预览)

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wing004-zhengfangti.dwg---(点击预览)

wing003-liang-120.dwg---(点击预览)

wing002-liang-30.dwg---(点击预览)

proe三维图.rar

proe运动仿真视频.rar

robo程序.rar

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关键词：: 仿生机器人机构设计运动仿真开题翻译答辩文献综述 proe三维图纸仿生机器人

资源描述：

仿生机器人的机构设计与运动仿真

43页 15000字数+说明书+答辩稿+开题报告+文献综述+外文翻译+proe三维图+34张CAD图纸【详情如下】

proe三维图.rar

proe运动仿真视频.rar

robo程序.rar

wing002-liang-30.dwg

wing003-liang-120.dwg

wing004-zhengfangti.dwg

wing005-挡块.dwg

wing006-接头销.dwg

wing007-liang-15.dwg

wing008-zhengfang-kong.dwg

wing009-zhuanjiao.dwg

wing010-杆.dwg

wing011-suo.dwg

wing012-zhengfang-cao.dwg

wing013-liang-60.dwg

wing014-liangshuangtou-15.dwg

wing015-bianzhengfang.dwg

wing016-杆.dwg

wing017-ka.dwg

wing018-销钉.dwg

wing019-changfang.dwg

wing020-zhengfang-shuangtou.dwg

wing021-齿套.dwg

wing022-封板.dwg

wing023-轴卡.dwg

wing025-键-销.dwg

wing026-板链接块.dwg

wing027-轴套.dwg

wing028-轴.dwg

wing029-杆.dwg

wing030-jiaozhang.dwg

wing031-齿轮.dwg

wing032-qvzhou.dwg

仿生机器人的机构设计与运动仿真开题报告.doc

仿生机器人的机构设计与运动仿真论文答辩稿.ppt

仿生机器人的机构设计与运动仿真说明书.doc

外文翻译---通用回归神经网络在声呐目标分类中的应用.doc

总装配图.dwg

文献综述.doc

机器人骨架.dwg

猪笼草.dwg

论文封面目录.doc

足装配图.dwg

QQ截图20140127092852.gif

wing021-齿套.gif

wing031-齿轮.gif

仿生机器人的机构设计与运动仿真.gif

机器人骨架.gif

全部文件1.gif

全部文件2.gif

猪笼草.gif

总装配图.gif

足装配图.gif

摘要I

ABSTRACTII

第1章绪论1

1.1 仿生机器人概述1

1.2仿生型多足步行机器人技术综述2

1.2.1国外仿生机器人研究现状2

1.2.2国内仿生机器人的研究现状4

1.3多足机器人的关键技术5

1.3.1协调控制问题6

1.3.2信息融合问题6

1.3.3机构设计问题6

1.3.4微传感和微驱动问题6

1.3.5能源问题6

第2章仿生机器人总体设计方案的确定8

2.1 概述机构模型8

2.2本体结构设计8

2.2.1六面连接体设计8

2.2.2步行足的结构模型9

2.2.3仿生六足虫机器人的整体结构10

2.2.4骨架的搭建11

第3章仿生机器人运动系统的设计12

3.1腿部的运动分析和设计12

3.2传动部分结构设计12

3.3步态规划及分析13

3.3.1关于步态的参数描述13

3.3.2 三角步态运动原理14

第4章控制系统的设计16

4.1控制的硬件系统设计16

4.2慧鱼 ROBO接口板介绍16

4.2.1 外形尺寸和重量16

4.2.2 电源9V直流，1000M16

4.2.3 处理器和存储器16

4.2.4 输出M1-M4或者O1-O816

4.2.5 数字量输入I1-I817

4.2.6 模拟阻抗输入AX和AY17

4.2.7 模拟电压输入A1和A217

4.2.8 距离传感器输入D1和D217

4.2.9 红外线（IR）输入17

4.2.10 USB接口和串口17

4.2.11 接口的选择18

4.2.12 端口的固定设置18

4.2.13 红外测试功能18

4.2.14 26针插槽18

4.2.15 I/O扩展板用插槽19

4.2.16 无线射频通信模块用插槽19

4.2.17对接口板的程序控制19

4.3 ROBO接口板与机器人的连接22

4.4软件系统22

4.4.1软件介绍22

4.4.2运动规划22

4.4.3程序设计23

第5章运动仿真26

5.1计算机仿真技术26

5.1.1基于proe的机器人运动仿真26

5.1.2实物仿真28

第6章总结与展望30

致谢31

参考文献32

摘要

　　　随着仿生学与机器人技术的飞速发展，仿生机器人已日益成为机器人领域的研究热点。本论文结合理论与实践，对仿生机器人的结构与控制系统进行了研究。

　　　本论文主要研究内容包括仿生机器人的总体方案设计、驱动系统与运动系统的设计、运动控制系统的软硬件设计。总体方案设计主要讨论了仿生机器人的机械本体结构，机器人足的结构设计。驱动系统和运动系统主要分析了腿部的运动,机器人的运动规划和驱动系统结构。运动系统硬件设计是采用的慧鱼ROBO接口板。软件设计是结合慧鱼公司开发的编程软件（robot pro）进行编程。运用PROE对机器人进行运动仿真，并通过试验实现了设计要求。　　　

关键词：仿生机器人，结构，控制，编程，运动仿真1.3.1协调控制问题

机器人的自由度越多，机构越复杂，必将导致控制系统的复杂化。复杂大系统的实现不能全靠子系统的堆积，要做到“整体大于组分之和”，同时要研究高效优化的控制算法才能使系统具有实时处理能力。

1.3.2信息融合问题

在仿生机器人的设计开发中，为实现对不同物体和未知环境的感知，都装备有一定量的传感器，多传感器的信息融合技术把分布在不同位置的多个同类或不同类的传感器所提供的局部境的不完整信息加以综合，消除多传感器信息之间可能存在的冗余和矛盾，从而提高系统决策、规划、反应的快速性和正确性。

1.3.3机构设计问题

　　　合理的机构设计是仿生机器人实现的基础。生物的形态经过千百万年的进化，其结构特征极具合理性，而要用机械来完全仿制生物体几乎是不可能的，只有在充分研究生物肌体结构和运动特性的基础上提取其精髓进行简化，才能开发全方位关节机构和简单关节组成高灵活性的机器人机构。

1.3.4微传感和微驱动问题

　　　微型仿生机器人作为仿生机器人中特殊的种类，绝不是传统常规机器人的按比例缩小，它的开发涉及到电磁、机械、热、光、化学、生物等多学科。对于微型仿生机器人的制造，需要解决一些工程上的问题。如动力源、驱动方式、传感器集成控制以及同外界的通讯等，实现微传感和微驱动的一个关键技术是机电光一体结合的微加工技术。同时，在设计时必须考虑到尺寸效应、新材料、新工艺等问题。

1.3.5能源问题

　　　要使机器人在相对较广的范围内完成较长时间的复杂工作，能源问题是不得不考虑的。目前，广泛作为无缆机器人能源的电池还不能满足机器人长时间，大范围的工作要求。可以说新能源的开发研究，对机器人研究有着重要的意义。

内容简介：: 重庆交通学院二OO五届毕业设计(论文) 译文通用回归神经网络在声呐目标分类中的应用BURCU ERKMEN TULAY YILDMM摘要：应用有源声呐在浅水区水下水雷的远距离探测从而维护重要海港和海岸线区域的安全性是必需、重要的科目。人类专家从事的从海底上的水雷和岩石反馈有源声呐进行的工作通常是十分困难而且工作量非常庞大。由于它的自适应和并行处理能力，神经网络分类器已经被广泛应用在复杂声呐信号的分类中。此文，通用回归神经网络应用于解决水下目标分类的问题，作为特征抽取的主元分析已经被建立起来提高分类性能。接受器工作特性分析在神经分类器上用来评估诊断程序的灵敏度和比功率上得以应用.1. 引言由于海洋环境的复杂性，以声呐信号为基础的水下信号的自动化识别的分类是一种复杂的问题。人类专家进行的鉴别工作通常不是一种简单的任务而且有十分庞大的工作量。具有自适应和计算优势的神经网络的出现是十分适合有源声呐的分类。Gorman和Sejnowski1所作的开创性论文有可能是记载神经网络在该领域应用的首篇论文。他们用简单特性的频谱作为神经网络分类器的输入去从定位在海洋底部沙层上柱状形的岩石中区分出水雷。在这些开创性的论文后，人们已经对利用网络进行声呐目标的自动识别产生了浓厚的兴趣。多层感知器分类器17、径向基函数网络3、8、通用回归神经网络9和随机性神经网络3、有效的前馈性神经网络在文献科研中已经广泛的应用在识别声呐信号里。特征抽取是进行复杂信号例如视觉、语音鉴别或者在声呐反馈里目标探测中被提及的问题的分类的重要预处理步骤。这些问题的输入维数对于分类来说变成不利因素，如主元分析和神经鉴别分析。特征抽取技术把高因次信号降低为在特征空间里保存最有利用价值和相关联信息的低因次特征置位。在10、11中，一些特征提取技术应用在预先给予的声呐信号中。在这篇论文中，通用回归神经网络已经被应用于鉴别在海洋底部沙层上的一个水雷和柱状形岩石两个不同目标的反馈声呐。这里重点不仅是分类器本身而且在于应用主元分析的特征抽取技术的方法，可以改善分类器的性能。接受器工作特性分析是一种在声呐研究中测试诊断性能的既定的方法。2. 关于Gorman和Sejnowski的数据 Gorman和Sejnowski1、2，采用的最初声呐数据。这些数据组是从加里福尼亚大学机械研究数据库采集中心取得的。这些数据包含从两个来源：一个金属柱面和相似形的岩石中收集来的声呐反馈，两个目标放在同一个沙层平面上，以不同的视界角向它们发射声呐线性固频脉冲，从而产生变差的数据。在G&S的实验里，数据经过过滤和从中提取60个抽样（输入或者我们的维数）的频谱包络线。这些数据包含有111个从远离不同视界角和不同条件下跳动出来的声呐信号中获取的图案和97个在类似条件下的岩石中获取的图案。数据组包含有从90度幅宽的柱面和180度幅宽的岩石的不同视界角变体中获取的数据。在这些数据文件里共有208种抽样，这些被挑选出来的抽样增加了视界角的数目。3. 通用回归神经网络在声呐目标分类中的应用通用回归神经网络是基于概率密度函数估计的，有记忆基础的前馈型网络。通用回归神经网络上一种径向基函数网络，它经常应用在函数概算中，快速导流数次通用回归神经网络的特性能进行非线性函数建模，已经在给予足够数据的噪声环境中其出色完成任务的实验中得到证明。最初它在统计文学中得到发展，又以纳达亚沃森积分方程的影响函数回归而闻名。在1990年Donald specht又重新发掘了它，通用回归神经网络拓扑结构包含四层：输入层、隐蔽层、总领层和输出层。通用回归神经网络的最大优点在于网络能被导流的速度，导流一种通用回归神经网络在一次完整的工作循环中可被完成。它不需要一种如在多层感知器那样的叠接导流程序，它近似一些在输入和输出向量之间的随机函数，可直接从导流数据中得出这函数的概率，而且它具有一致性，那就是，当导流设备的尺寸过大时伴随着对函数的微弱干扰，其估计错误率近似为零。像在标准回归方法中，通用回归神经网络应用在连续变量的估计中，它涉及到径向基函数网络，基于一种叫做拓扑结构回归的标准型统计方法。通用回归神经网络体系结构如数字图1所示，有关通用回归神经网络体系结构和一些数学计算方面的详细信息在Specht的论文3中可得到验证。数字图.1. 通用回归神经网络体系结构4.传统主元分析主元分析是一种数据压缩和特征抽取的多变量统计分析方法，需要分类器在线操作中有直观实际应用。通过发送输出数据到m导向偶极子数量的简化上从而降低n维输入空间的维数,对于分类器设计来说其是一种有吸引力的方法，更便利于分类器的工作。主元分析在特征向量（从处理协变式的矩阵中计算）的基础中描绘原始数据空间，在对特征向量指示器处理过的能量进行相应的特征值计算中，考虑到最高特性值的m特征向量被数据传送所限制,一种有效的最少信息丢失的原始输入空间的维次简化方法被实现啦！有关主元分析详细计算可参阅14。5.仿真结果研究的目的是使通用回归神经网络从事于声呐反馈的鉴别，从而改善其分类性能和简化网络的复杂性。主元分析作为特征抽取方法被应用，通过MATLAB 6.0 和神经网络工具箱完成分类工作。数据组包含有208条反馈信息（其中111条柱形水雷反馈信号和97条岩石反馈信号），全部数据组等信号区分为随机训练组和测试组，（103种抽样为训练文件、105种抽样为测试文件）。此处作用的网络是一种全相连的前馈型包含60个输入波和1个输出波的神经网络。研究的第一步，不含应用特征抽取方法的通用回归神经网络已经被应用在声呐数据中。扩展参数的抉择十分适合通用回归神经网络的特性，扩展数值选在0.06最佳，不含运用主元分析的通用回归神经网络分类器所实现的性能，如表一所示：表一：不含运用主元分析的通用回归神经网络的正确分类百分率等级总数“水雷”评估正确率% “岩石”评估正确率% 分类评估正确率%训练100 训练100 训练100测试92.06 测试90.48 测试91.42第二步，主元分析已经应用在了有60维数输入空间的数据组中。当运用主元分析处理60维数输入空间简化为20维数的输入空间时，关于测试结果的最大准确度实现时，相同扩展数值通用回归神经网络分类器的分类性能如表二所示：表二：含运用主元分析的通用回归神经网络分析正确分类百分率等级总数“水雷”评估正确率% “岩石”评估正确率% 分类评估正确率%训练100 训练100 训练100测试93.65 测试92.85 测试93.33表一与表二的结果相比显示，通用回归神经网络分类器的分类性能通过运用主元分析可得到改善，同时，主元分析提供用于在线通用回归神经网络分类器操作使计算的复杂性得到简化。最后，接受器的工作特性以及被应用在通用回归神经网络分类器的测试结果中，接受器的工作特性分析在声呐探中的评估标准，表明探测的可能性交替使用与误差探测可能性的比值。对接受器的工作特性分析的诊断对测试差值来讲灵敏度和特征值是基本的表达式。计算如表三所示，表三中左边为分类结果，上部为有/无水雷状况。表三: 诊断测试解析表水雷扫描岩石扫描“水雷”分类器的结果真实值理想值“岩石” 分类器的结果错误偏差真实偏差真实值:59 理想值:3 错误偏差:4 真实偏差:39灵敏度=比功率=6.结论此文，通用回归神经网络已经应用于鉴别从两个来源：一个金属柱状物和相似形岩石收集来的水下目标。这里重点不仅是分类器本身而且是在于用主元分析改善分类器性能的特征抽取方法的处理。通过主元分析实现的简化计算复杂性的方法有助于小型神经分类器的设计开发。这种分类器设计有利于在线操作和在硬件方面可得到保证。当通用回归神经网络就分类成功率而论的运用特性相比以前的研究时，相比那些最大准确率可达90.4%1和接受器工作性能分析相似于1（灵敏度和比功率）所示的分类可靠性而言时其效果更好。参考文献：1. Gorman R.P.;Sejnowski T.J.,(1988). Learned classification of sonar targets using a massively parallel network. IEEE Transaction on Acoustics, Spech, and Signal Processing, Vol. 36, No.7,pp. 1135-1140.2. Gorman R.P.;Sejnowski T.J.,(1988). Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netwoeks, Vol 1,No1,pp.75-89.3. Chen, C.H.(1992). Neural Netwoeks for active sonar classification. Pattern Recogceedings., Vol.II. Conference B :Pattern Recognition Methodology and Systems, Proceedings.,11th IAPR International Conferene on,pp. 438-440.4. Diep, D.; johannet, A.; Bonnefoy, P.; Harroy, F.; Loiseau, P., Classification of sonar data for a mobile robot using nearal network .(1998) Intelligence and Systems, Proceedings., IEEE International Joint Symposia on, pp. 257-260.5. Haley ,T.B.,(1990). Applying nearal network to automatic activ classification. Pattern Recognition, Proceedings ., 10th International Conferene on,Vol: 2, pp. 41-446. Shazeer, D.J.; Bello, M.G., Minehunting with multi-layer perceptrons. (1991) Neural Network for Ocean Engineering ,1991, IEE Conference on ,pp. 57-68.7. YuWang Jing; EI-Hawaey, F.;A multilayered ANN architecture for underwater target tracking.(1994). Electrical and Computer Engineering , Conference Proceedings. 1994 Canadian Conference on, Vol .2pp. 785-788.8. Yegnanaray,B.; Chouhan,H.M.; Chandra Sekhar, C., sonar target recoginting using radial basss functiong networks. (1992). Singapore ICCSISITA 92. Communications on the Move, vol.1,pp.395-399.9. Kapanoglu B., Yildmm T., Generalized Regression Neural Netwoeks Ror Underwater target Classification. Neu-Cee2004 2nd International Symposia on Electrical and Computer Engineering, Nicosia ,North Cyprus, pp:223-225.10. Soares Filho, W.; Manoel de Seixas, J.;Perira Caloba, L.; Principal componefnt analysis for classifying passive sonar sigals.(2001). Circuits and Systems ,ISCAS 2001. The 2001 IEEE International Symposia on,Vol. 3, pp. 592-595.11. Larkin ,M.J.; Optimal feature extraction techniques to improve Classification performance , with application to sonar signals .(1997) Neural Networks for Signal Processing VII .Proceedings of the 1997 IEEE Workshop ,pp. 64-71.12. Woods, k.s.; bowyer,K.W., Generating ROC curves for artificial neural networks .(1994) Computre-Based Medical Systems, Proceedings 1994 IEEE Seventh Symposium on, pp. 201-206.13. Specht D.F.,(1991) Ageneralized regression neuralnetwoeks . IEEE Transations on Neural Networks .Vol. 6, pp. 568-576.14. Song W.; Shaowei X., ROBUST PCA Based on Neural Networks .(19970 Proceedings of the 36th Conferenc on Decision&Control San Diego , California USA ,pp. 译文原文：Sonar Target Classification UsingGenerralRegression Neural NetworkBURCU ERKMEN TULAY YILDMMAbstract: The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors cost line areas. The discrimination active sonar returns form mines and returns form rocks on the sea floor by human experts is usually difficult and very heavy workload. Neural network classifiers have been widely used in classification of complex sonar signals due to its adaptive and parallel processing ability. In this paper General Regression Neural Network (GRNN) is used to solve the problem of classification underwater targets. Principal Component Analysis (PCA) has been established as a feature extraction method to improve classification performance.Receiver Operating Characteristic (ROC) analysis has been applied to the neural classifier to evaluate the sensitivity and spscificity of diagnostic procedures.1.Introduction Automatic identification and classification of underwater signals on the basis of sonar signals are complex problem due to the complexity of the ocean environment. Identification by human experts is usually not objective and a very heavy workload. Neural Networks with their adaptive and computational advantages appear to be ideally suited to active sonar classification. The pioneer paper by Gorman and Sejnowski 1,2 was perhaps the first papers which reported the application of neural networks to this area. They used simple spectral features as the input to the neural-network classifier in order to distinguish a mine form cylindrical shape rock positioned on a sandy ocean floor. After these pioneer papers, these has been growing interest in the use of networks for the automatic recognition of sonar targets. Multi-lay Perceptron (MLP) classifier 1-7, Radial Basis Function Networks (RBFN) 3,8,General Regression Neural Networks 9, and the Probabilistic Neural Networks (PNN) 3 have been the efficient Feed-Forward Neural Networks widely used to classify sonar signal in literature.Feature extraction is the important preprocessing step to classification of complex signal such vision , speech identification or the problem mentioned here of detecting objects in sonar returns. Input dimensionality of these problem becomes a serious drawback to classification. Feature extraction techniques such as PCA, and Neural Discriminating Analysis (NDA) reduce a high dimensional signal to a lower dimensional feature set which preserves the most useful and relevant information on the feature space. In 10 ,11, several feature extraction techniques were used to preprocess given sonar signals.In this work , General Regression Neural Network (GRNN) has been used to classify sonar returns form tow different targets on the sandy ocean bottom a mine and cylindrical shaped rock. The emphasis in here is not classifier itself but also the process of the feature extraction technique , used PCA , improves classifier performance. In this paper, the ROC (Receiver Operating Characteristic ) analysis 12 has also been applied to the neural classifier to show the correctness of classification . ROC analysis is an established method of measuring diagnostic performance in sonar studies.2.About G orman and Sejnowski DataThe dataset, which is the original data used by Gorman and Sejnowski 1,2, was taken from the University of California collection of machine-learning-databases.The data consist of sonar returns collected form tow sources: a metal cylinder and similarly shaped rock. Both objects were lying on a sand surface,and the sonar chirp projected at them form different angles (aspect-angles) produced the variation in the data. In G&S experiment, the data was filtered and spectral envelope of 60 samples patterns obtained by bouncing sonar signals off a metal cylinder at various angles and under varivious conditions and 97 patters obtained form rocks under similar conditions. The transmited sonar signal is a frequerce-modulated chirp,rising in frequercy, The data set contain signals obtained form a variety of different aspect angles, spaaing 90 degrees for the cylinder and 180 degrees for the rock . There are 208 sampes in total in the data file ,which are sorted in increasing order of aspect-angle.3.General Regression Neural Network (GRNN)General Regression Neural Network (GRNN) are memory-based feed forward networks based on the estimation of probability density functions. GRNN is a kind of Radial Basis Function network (RBF) and it is ofen used for function approximation. GRNNs feature fast training times , can model non-linear function ,and have been shown to perform well in noisy environments given enough data . Originally developed in the statistics literature and known as Nadaraya-Watson kernel regession . It was re-discovered by Donald Specht in 1990. The GRNN topologfy consists of 4 layers : the input layer, the hidden layer , the summation layer, and the output. The primary advantage of the GRNN is the speed at which the network can be trained . Training a GRNN is performed in one pass. It dose not require an iterative training procedure as in MLP. It approximates any arbitrary function betweet input and output vectors, drawing the function estimate directly from the training data. Furthermore, it is consistent; that is, as the traing set size becomes large, the estimation error approaches zero; with only mild restrictions on the function. The GRNN is used for estimation of continuous variables, as in standar regression techniques. It is related to the radial basis function network and is based on a standard statistical technique called kernel regression. GRNN architecture is show in Figure 1. The detail information about GRNN architecture and some mathematical calculations can be examined in the Specht paper 13.Fig.1. GRNN architecture4.Traditional Principal Component AnalysisPrincipal Component Analysis is a multivariable ststistical analysis technique of data compression and feature extraction. Envisaging practical applications that require online operations for the classifier, an attractive approach for the classifier design is to reduce the dimensionalityof the N-dimensional input space by projecting input data onto a reduced a number of M diretions (MN) that can facilatate the classification task. The PCA describes the original data space in a base of eigenvectors ( computed from process covariance matrix ). The corresponding eigenvalues account for the energy of the process in the eigenvector directions. Considering data projection restricted to the M eigenvectors with highest eigenvalues, an effective reducetion on dimensionalality of original data input space can be achieved, with minimum information loss 10 . The detail calculations about PCA can be found in 14.5.Simulation ResultsThe aim of this study is to employ General Regression Neural Networks to classify sonar returns. To improve classification performance and simplify network complexity, PCA is used as feature extraction method. Classification was performed by using MATLAB 6.0 and Neural Network Toolbox. The data set consists of 208 returns (111 cylinder shaped mine returns and 97 rock returns). The entire data set was split into randomly train and test sets (103 samples for training file, 105 samples for test file ). The network used here was based on a fully-connedcted feed-forward neunal network composed of 60 input nodes and an output node.In the first step of this study, GRNN without using feature extraction method has been applied sonar data. Performance of GRNN is affected by the choice of spread parameter ,The spread value was optimally found as 0.06. The achieved performance parameter by GRNN classifier, without using PCA ,is given in Table 1. Table 1. The percentages of correct classification for GRNN without suing PCA CLASSES OVERALL %of correct %of correct %of correct “mine” estimation “rock” estimation classificationTrain Test Train Test Train Test 100 92.06 100 90.48 100 91.42In the second step ,PCA has been applied to the data set which has 60-dimensional input space. The maximun accuracy on the test results is achieved when 60-dimensional input spoace is reduced to 20-dimensional input space PCA preprocessing. Table 2 shows the classification performances of the GRNN classifier foy the same spread value.Table 2. The percentages of correct classification for GRNN with suing PCA CLASSES OVERALL %of correct %of correct %of correct “mine” estimation “rock” estimation classificationTrain Test Train Test Train Test 100 93.65 100 92.85 100 93.33Comparison the results of the Table 1 and Table 2 demonstrates that classification performance of GRNN classifier can be improved suing PCA. Fuethmore , the low computational COMPLEXITY Achieved by PCA provides the GRNN classifier for online operations.Finally, ROC analysis has been applied to test results of GRNN classifier. The evaluation criteria of ROC analrsis in sonar detection indicate the trade-off of probability of detection versus probability of fasle detection. Sensitivity and specificity are the basic expressions (Eq.1.and Eq.2.) for the diagnostic test interpretation of the ROC analysis . Table 3is employed for these calculations .Table 3 labeled with classification results on the left side and mine absent/present status on the top . Mine Present Rock Present The Result of Classifier “Mine” True Positives (TP) Fasle Positives (FP)The Result of Classifier “Rock”Fasle Positives (FP) True Positives (TP)Table 3. Diagnostic test interpretation table TP: 59 FP: 3 FN: 4 TN: 39 number of true positiveSensitivity= =0.9365 number of true positive + number of false negatives number of true negativesSpecificity=0.9285 number of true negatives + number of false negatives6.CONCLUSIONIn this paper , a GRNN has been used to classify underwater target collected from two sources: a metal cylinder and similarly shaped rock . The emphasis in here is not classifier itself but also the process of the feature ex traction technique, used PCA , improves classifier performance. The low computational complexity achieved by PCA helps designing compact neural classifier ,which is attractive for online operations and hardware realizations. When the performance of GRNN using with PCA are compared with the previous studies in terms of successful classification rates, this result is better than those with the accuracies of maximum 90.4% 1 and ROC analysis results which is close to 1 (sensitivity and specificity ) shows the reliability of this classification.Refernces 15. Gorman R.P.;Sejnowski T.J.,(1988). Learned classification of sonar targets using a massively parallel network. IEEE Transaction on Acoustics, Spech, and Signal Processing, Vol. 36, No.7,pp. 1135-1140.16. Gorman R.P.;Sejnowski T.J.,(1988). Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netwoeks, Vol 1,No1,pp.75-89.17. Chen, C.H.(1992). Neural Netwoeks for active sonar classification. Pattern Recogceedings., Vol.II. Conference B :Pattern Recognition Methodology and Systems, Proceedings.,11th IAPR International Conferene on,pp. 438-440.18. Diep, D.; johannet, A.; Bonnefoy, P.; Harroy, F.; Loiseau, P., Classification of sonar data for a mobile robot using nearal network .(1998) Intelligence and Systems, Proceed

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