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基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the
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采用BP神经网络算法进行调制样式识别的Matlab代码-Using BP neural network pattern recognition algorithm Matlab code modulation
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基于神经网络的语言情感识别系统，可以识别人讲话时是喜，怒，高兴还是平静，具有较高的识别率。-The language of emotion recognition system based on neural network can recognize human speech is happy, angry, happy or calm, has a high recognition rate.
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基于概率神经网络的手写体数字识别，人工智能方面的应用-Handwritten numeral recognition based on probabilistic neural networks, artificial intelligence applications
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arallel implementation, and its portfolio optimization, machine learning, image processing, process control, evolutionary neural networks, Fuzzy Pattern Recognition and Artificial Life
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BP neural network function fitting and pattern recognition, MinkowskiMethod algorithm, Including quaternion various calculations.
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Clustering analysis based on Euclidean distance, BP neural network function fitting and pattern recognition, A noise auxiliary data analysis method.
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Includes the modulation, demodulation, signal to noise ratio calculation, BP neural network function fitting and pattern recognition, Calculation of growth, entry-level program grain.
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Contains a common array signal processing algorithm, To achieve the recognition of 10 digital sound, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.
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The entire training process BP neural network, Complete HMM-based speech recognition system, This program has exceeded the performance of other algorithms.
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BP neural network function fitting and pattern recognition, Can be widely used in data analysis and forecast data, Simulation of the effect is very good.
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BP neural network function fitting and pattern recognition, Join repetitive control, Use of natural gradient algorithm.
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The performance of the program has reached a high level, BP neural network function fitting and pattern recognition, Mainly based on the mtlab procedures.
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第一次作业_基于分类算法的雷达状态识别
对于本数据集中的雷达状态识别，数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响；数据降维后使用神经网络算法最优，支持向量机算法其次，朴素贝叶斯算法较差。此外，训练样本越多，分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition
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