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SYSTEM FOR DETECTION AND CLASSIFICATION OF SEA TARGETS USING MATHEMATICAL MODEL OF TARGET TYPE DETERMINATION

机译:目标类型确定数学模型的海洋目标确定与分类系统

摘要

FIELD: hydro acoustics.;SUBSTANCE: invention relates to underwater acoustics and can be used to build intelligent automated systems for classifying marine targets, detected by the signs of amplitude-phase modulation of low-frequency signals of the pumping of the marine environment by radiation and fields of objects. System for detecting and classifying sea targets using a mathematical model for determining the target type contains a working zone of nonlinear interaction formed in the sea medium and parametric conversion of pumping waves and information waves. Principal difference from prototype is that additionally introduced is an adaptive neuro-fuzzy correction path, comprising a unit of productive rules and functions, the input of which is connected to the output of the neural network recognition and classification channel learning unit, and the output is connected to the input of the neuro-fuzzy network adapter, the function of which is carried out by the adaptive neuro-fuzzy network (ANFIS), and enveloped by the feedback with the differentiator, wherein the output of the neuro-fuzzy network adapter is connected to the input of the differentiator, the output of which is connected to the input of the fuzzy controller, self-adjusting its rule base based on a selection of mathematical models of marine targets, at the output of which the new production rule number signal is generated, as well as a new type of membership function of the target type for the neural network recognition and classification path learning unit, further at the target identification and target recognition unit output by the amplitude-frequency characteristics, neural network recognition and classification channel providing a final classification solution based on detected sea targets, a signal is generated according to the type of target according to the degree of belonging of the analyzed spectrum to the classification object.;EFFECT: automation of the process of recognizing classes of sea targets (surface or underwater object), detected by signs of amplitude-phase modulation of low-frequency pumping signals of the marine environment by radiation and field objects, complex reduction of data size during automatic adjustment of rules base due to generation and reduction of sampling of reference samples of mathematical models of sea targets carried out by means of tract adaptive neuro-fuzzy correction, required for implementation of final classification process in neural network recognition and classification channel, which provides higher probability of correct classification of marine target (surface or underwater object) by 5–7 %.;1 cl, 7 dwg
机译:技术领域本发明涉及水下声学,并且可以用于构建用于对海洋目标进行分类的智能自动化系统,该系统通过辐射对海洋环境的低频信号的振幅相位调制的信号进行检测。和对象领域。使用用于确定目标类型的数学模型对海洋目标进行检测和分类的系统包含在海洋介质中形成的非线性相互作用以及抽水波和信息波的参数转换的工作区域。与原型的主要区别在于,另外引入的是自适应神经模糊校正路径,包括生产规则和功能单元,其输入连接到神经网络识别和分类通道学习单元的输出,并且输出是连接到神经模糊网络适配器的输入,该神经模糊网络适配器的功能由自适应神经模糊网络(ANFIS)进行,并由微分器的反馈进行封装,其中神经模糊网络适配器的输出为连接到微分器的输入,微分器的输出连接到模糊控制器的输入,并根据海洋目标数学模型的选择对自身的规则库进行自调整,在该模型的输出处会生成新的生产规则编号信号生成,以及神经网络识别和分类路径学习单元的目标类型的新型隶属函数,进一步在目标处通过振幅-频率特性,神经网络识别和分类通道输出的识别和目标识别单元,根据检测到的海洋目标提供最终的分类解决方案,根据目标的类型根据分析物的归属程度生成信号效果:自动识别海洋目标类别(表面或水下物体)的过程,该过程由辐射和野外物体对海洋环境的低频泵浦信号进行幅度相位调制的迹象来检测,由于通过神经网络识别的最终分类过程的实施,通过道自适应神经模糊校正进行的海洋目标数学模型参考样本的生成和采样减少,规则自动调整期间数据大小的复杂减少和分类渠道,提供更高的概率正确分类海洋目标(水面或水下物体)的比例为5%至7%。; 1 cl,7 dwg

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