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In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC (cerebellar model articulation controllers) neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved. 相似文献
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板形模式识别的GA-BP模型和改进的最小二乘法 总被引:11,自引:0,他引:11
针对板宽变化时需要不同拓扑结构的神经网络才能完成板形模式识别任务,网络学习工作量大,网络存在收敛速度慢,易陷入局部极小等结构性能不佳的问题,首次建立了以勒让德正交多项式为基模式的只用6个输入信号、3个输出信号的板形模式识别GA-BP网络模型。该模型不仅结构简单,而且物理意义明确,识别精度较高,解决了板宽变化时神经网络结构形式不变的问题,从而实现了板形模式识别的智能化。又提出了基于勒让德正交多项式的板形模式识别最小二乘法,该方法简单、实用,识别精度较高,克服了传统的最小二乘模型板形模式识别的缺点和不足。为板形模式识别提供了两种简便实用的新方法,发展了板形模式识别理论和方法。 相似文献
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A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression 总被引:1,自引:0,他引:1
To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to overcome the defects live in the existent recognition methods based on fuzzy, neural network and support vector regression (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVR) was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhancing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LS-SVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability. 相似文献
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A Neural Network Recognition Method of Shape Pattern 总被引:8,自引:0,他引:8
The final objective of shape control is to makethe output shape data achieve the objective shapedata,then the adjusting magnitude of shape controlactuating mechanism can be defined in accordancewith the deflection value of the output shape dataand the objective shape data.In shape controlsystem,the first thing is to make shape faultpattern recognition based on the speciality andrequirement of shape control actuating mechanism ofrolling mill.The main assignment of shape patternrecognition is to… 相似文献
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In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value. 相似文献
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智能识别方法在板形识别中的应用及发展趋势 总被引:2,自引:0,他引:2
对国内外关于板形模式识别技术的研究现状和发展趋势进行了综述。通过比较,提出了传统的基于最小二乘法的多项式拟合法存在的不足,并对模糊分类、神经网络、遗传算法、混沌优化等智能识别方法在板形模式识别中所具有的优势进行了归纳和总结。最后,对智能方法在板形识别问题中的应用以及板形识别技术的发展趋势进行了展望,为板形检测环节得到理想板形信号提供理论研究方法,并将逐步应用于钢铁板形控制的工业过程中。 相似文献
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冷轧板形控制系统是一个强耦合、非线性的多变量复杂系统,难以建立精确的数学模型,一般常规的控制方法难以取得令人满意的控制效果。本文依据现场的轧制数据,提出采用自适应竞争遗传算法优化神经网络对其进行建模,采用模糊控制,可实现实时控制,并利用MATLAB编程,仿真结果显示了算法的有效性和时效性。 相似文献
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变结构神经网络在板形信号模式识别方面的应用 总被引:13,自引:0,他引:13
提出了基于模糊距离的变结构神经优化算法,并将其用于板形信号的模式识别过程,有效地解决了板宽变化时神经网络拓扑结构不变的问题,提高了识别速度和精度,从而成为一种新的智能板形信号识别方法。 相似文献
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针对目前的板形缺陷识别方法精度不高、识别速度慢的问题,根据Elman神经网络模型可以反映系统动态特性,而且可以逼近任意非线性函数的特点,提出了一种利用改进的遗传算法优化Elman神经网络,使其泛化能力强、学习速度快、识别精度高,并建立板形缺陷模式识别模型的方法。为了验证该方法的识别能力,在隐层节点数与学习次数相同的条件下,分别与遗传算法优化的Elman网络和BP网络模型进行板形识别仿真对比分析。试验结果表明,改进遗传算法优化的Elman神经网络模型对板形缺陷识别精度高于BP网络等模型,并且具有收敛速度快的优点。 相似文献
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对神经网络和传统模式识别技术的基本原理和应用特点进行比较.根据已有烧结合成O'-Sialon的实验数据,利用改进后的人工神经网络,构筑了材料相组成及性能的预测模块,并与模式识别技术处理得到的结果进行了比较.在此基础上,探索了合成O'-Sialon-BN复合材料的工艺条件,并通过实验结果进行了验证. 相似文献