基于人工神经网络的混合智能系统研究及应用
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摘要
本次课题的核心内容和研究思路可概括如下：分析舰艇自噪声的非平稳特征,针对ANC系统的弊端探讨解决方案；设计并完善以人工神经网络为框架的自适应噪声抵消系统；引入模糊逻辑推理思想,设计更加智能化的网络结构；借助多分辨率分析的思想,进一步完善网络的逼近能力和自适应性；引入遗传进化理论,结合强有力的局部算法构造自适应混合学习策略；最后通过仿真试验和真实海试数据处理,系统的验证文中结论。
目标信号所处的声场环境,决定了ANC系统的非线性特征,针对待抵消信号的非平稳性,我们在传统非线性滤波的研究基础上,设计人工神经网络作为自适应处理器,依靠其强大的与人脑类似的功能,来解决常规滤波模式的固有缺陷。在系统自适应学习中,神经网络将非线性映射的问题转化为求解优化问题,而优化过程依靠其自学习、并行处理等优势,通过简便的学习算法来实现。RBF神经网络的引入,更使得系统求解复杂、高度非线性的能力大大提高。文中所设计的模型为非线性系统问题的解决奠定了良好的基础,且算法计算的复杂程度适中,具有较高的应用价值。
本文采用T-S模糊推理模型构建分段线性模型,作为网络系统的基础构架,将输入空间分为若干个模糊子空间。再通过模糊系统和人工神经网络的等价性特征,把该模糊推理模型表现为一个自适应神经网络的形态,使得该网络系统的所有结点和参数都对应实际意义。同时,再将传统T-S模型与多分辨率理论(MRA)相结合,将WNN放入模糊推理后件中,使得模型中每一个模糊规则相当于一个子小波网络。然后引入新的自适应混合遗传算法,用于网络结构和各个参量的学习修正,以提高系统的运行效率和求解质量。数据处理结果证明这种融合了多项新颖设计思想的混合智能系统(HIS),系统进化的思路清晰易懂,并且有效的提高了系统降噪性能。
The core content and research method of this paper can be summarized as follows:analyzing the non-stationary characteristics of ship self noise to explore solutions of disadvantages of ANC system; designing and improving adaptive noise cancellation system based on artificial neural networks; importing fuzzy logic reasoning thought to design more intelligent network structure; further improving the network's approximation capacity and adaptive performance using the idea of multi-resolution analysis; importing genetic evolution theory combined with strong local structure algorithm to construct adaptive mixed-learning strategies; verifying the conclusions of the paper by Simulation and the real sea trial data-processing systemically.
The target signal's acoustic field environment decided the non-linear features of ANC system. Artificial neural network adaptive processor was designed based on traditional non-linear filter against the non-stationary signals. Its strong functions similar to the human brain were used to solve the inherent flaws of conventional filtering mode. Neural network transformed the problem of non-linear mapping into the problem of optimization solving in system's adaptive learning. The optimization process can be realized by simple learning algorithm relying on advantages such as its self-learning and parallel processing. The introduction of RBF neural network enhanced system's capacity of solving complex and highly non-linear problem greatly. The model designed in this paper gave a good foundation for solution of non-linear system problem. It also had middle computing complexity and high application value.
T-S fuzzy reasoning model was used to construct sub-linear model. It was used as the network infrastructure and divided the input space into a number of fuzzy subspaces. Through fuzzy systems and artificial neural network equivalent features, fuzzy reasoning model performed as an adaptive neural network. This made network's all the nodes and the corresponding parameters have practical significance. At the same time, every fuzzy rules was equal to a wavelet sub-network by combining traditional T-S and MRA and putting WNN into after-pieces of fuzzy reasoning. Then new adaptive hybrid genetic algorithm was imported for network and parameters amendments to improve the operating efficiency of the system and solve quality. Data processing results showed that the modified HIS make the system evolution easier to understand and effectively improve the system performance.
目标信号所处的声场环境,决定了ANC系统的非线性特征,针对待抵消信号的非平稳性,我们在传统非线性滤波的研究基础上,设计人工神经网络作为自适应处理器,依靠其强大的与人脑类似的功能,来解决常规滤波模式的固有缺陷。在系统自适应学习中,神经网络将非线性映射的问题转化为求解优化问题,而优化过程依靠其自学习、并行处理等优势,通过简便的学习算法来实现。RBF神经网络的引入,更使得系统求解复杂、高度非线性的能力大大提高。文中所设计的模型为非线性系统问题的解决奠定了良好的基础,且算法计算的复杂程度适中,具有较高的应用价值。
本文采用T-S模糊推理模型构建分段线性模型,作为网络系统的基础构架,将输入空间分为若干个模糊子空间。再通过模糊系统和人工神经网络的等价性特征,把该模糊推理模型表现为一个自适应神经网络的形态,使得该网络系统的所有结点和参数都对应实际意义。同时,再将传统T-S模型与多分辨率理论(MRA)相结合,将WNN放入模糊推理后件中,使得模型中每一个模糊规则相当于一个子小波网络。然后引入新的自适应混合遗传算法,用于网络结构和各个参量的学习修正,以提高系统的运行效率和求解质量。数据处理结果证明这种融合了多项新颖设计思想的混合智能系统(HIS),系统进化的思路清晰易懂,并且有效的提高了系统降噪性能。
The core content and research method of this paper can be summarized as follows:analyzing the non-stationary characteristics of ship self noise to explore solutions of disadvantages of ANC system; designing and improving adaptive noise cancellation system based on artificial neural networks; importing fuzzy logic reasoning thought to design more intelligent network structure; further improving the network's approximation capacity and adaptive performance using the idea of multi-resolution analysis; importing genetic evolution theory combined with strong local structure algorithm to construct adaptive mixed-learning strategies; verifying the conclusions of the paper by Simulation and the real sea trial data-processing systemically.
The target signal's acoustic field environment decided the non-linear features of ANC system. Artificial neural network adaptive processor was designed based on traditional non-linear filter against the non-stationary signals. Its strong functions similar to the human brain were used to solve the inherent flaws of conventional filtering mode. Neural network transformed the problem of non-linear mapping into the problem of optimization solving in system's adaptive learning. The optimization process can be realized by simple learning algorithm relying on advantages such as its self-learning and parallel processing. The introduction of RBF neural network enhanced system's capacity of solving complex and highly non-linear problem greatly. The model designed in this paper gave a good foundation for solution of non-linear system problem. It also had middle computing complexity and high application value.
T-S fuzzy reasoning model was used to construct sub-linear model. It was used as the network infrastructure and divided the input space into a number of fuzzy subspaces. Through fuzzy systems and artificial neural network equivalent features, fuzzy reasoning model performed as an adaptive neural network. This made network's all the nodes and the corresponding parameters have practical significance. At the same time, every fuzzy rules was equal to a wavelet sub-network by combining traditional T-S and MRA and putting WNN into after-pieces of fuzzy reasoning. Then new adaptive hybrid genetic algorithm was imported for network and parameters amendments to improve the operating efficiency of the system and solve quality. Data processing results showed that the modified HIS make the system evolution easier to understand and effectively improve the system performance.
引文
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