發(fā)布時間：2018-07-08 10:27

  本文選題：小波神經(jīng)網(wǎng)絡 + 參數(shù)初始化�。� 參考：《中北大學》2015年博士論文

【摘要】：小波分析與神經(jīng)網(wǎng)絡都是新一代計算智能信息處理技術的主要組成部分。小波變換是一個時間域和頻率域的局部變換,利用對小波函數(shù)的伸縮平移運算對信號進行不同尺度下的分析,可以有效地從信號中提取有用信息。它克服了傳統(tǒng)傅里葉變換不能同時進行時頻分析的缺陷,因而成為了非線性科學的前沿技術之一。人工神經(jīng)網(wǎng)絡是通過對人腦神經(jīng)網(wǎng)絡的合理抽象而形成的理論化模型,它可以依靠自身對環(huán)境的自學習能力獲取知識,并利用神經(jīng)元之間的連接權值存儲獲取的知識。小波神經(jīng)網(wǎng)絡(簡稱小波網(wǎng)絡)是小波分析與神經(jīng)網(wǎng)絡相結合的產(chǎn)物,它可以一方面保持神經(jīng)網(wǎng)絡的多輸入并行處理能力、自學習能力、非線性映射和容錯能力等,另一方面將具有強數(shù)學基礎的小波分析方便地應用于高維問題,有效地發(fā)揮二者的優(yōu)勢。本文在研究了已有經(jīng)典小波神經(jīng)網(wǎng)絡模型的基礎上,以前饋式小波網(wǎng)絡為主要研究對象,對具有張量積型小波函數(shù)的自適應小波網(wǎng)絡的初始化、與模糊機制結合的模糊小波網(wǎng)絡構造與算法、徑向小波網(wǎng)絡的算法等方面做了較為深入的研究與分析,并將其應用于信號預測,系統(tǒng)辨識,模式分類等方面,使得這幾類小波網(wǎng)絡在應用方面具有更高的實用價值與意義。具體工作主要有:首先,具有張量積型小波激活函數(shù)的多維輸入自適應小波網(wǎng)絡(WNN_M)是最早也是最經(jīng)典的小波網(wǎng)絡模型,其尺度參數(shù)、平移參數(shù)及線性權值均為網(wǎng)絡可調參數(shù),應用中增加了網(wǎng)絡的靈活性,使得用更小規(guī)模的小波網(wǎng)絡逼近非線性系統(tǒng)成為可能,在其應用中,初始參數(shù)的設置仍多采取Q.H.Zhang提出的啟發(fā)式方法(HIA)。為了得到針對該模型的更理想的參數(shù)初始值,使得網(wǎng)絡訓練能夠快速收斂,提出了一種利用隱層小波函數(shù)時頻局部化特點的改進參數(shù)初始化方法(CIA)。該方法首先根據(jù)多維輸入數(shù)據(jù)的統(tǒng)計特征設定閾值向量,利用最鄰近聚類的思想確定網(wǎng)絡隱層神經(jīng)元個數(shù),并由聚類后的類中心與半徑,通過隱層小波函數(shù)的時間窗寬確定相應的小波平移參向量與尺度參向量的初始值。在對三類不同的時間序列預測的實驗中看出,相比于WNN_M-HIA,WNN_M-CIA的初始誤差明顯較低,在經(jīng)過梯度下降法的數(shù)次迭代后,其預測精度仍優(yōu)于基于HIA初始化的網(wǎng)絡預測精度,說明了該初始化算法的有效性和合理性。其次,考慮到將模糊理論與小波分析、神經(jīng)網(wǎng)絡結合研究的趨勢,提出了一種將TSK模糊系統(tǒng)與小波網(wǎng)絡結合的模糊小波網(wǎng)絡模型。該模型將具有連續(xù)參數(shù)和張量積型多維小波激活函數(shù)的小波網(wǎng)絡作為TSK模糊系統(tǒng)的結論部分,分析了與已有模糊小波網(wǎng)絡模型的區(qū)別,并將其應用于非線性系統(tǒng)辨識。在訓練算法方面,選擇使用基于粒子群算法和在線梯度下降法結合的混合優(yōu)化算法。通過對兩個非線性系統(tǒng)進行辨識實驗,可以看出,即使采用了較少的模糊規(guī)則和參數(shù)規(guī)模,相比于已有的模型,本文提出的模型與算法仍能得到更加滿意的辨識結果。再次,基于含有競爭算法的Kohonen自組織映射(SOM)神經(jīng)網(wǎng)絡,提出了一種對車牌圖像進行傾斜校正與字符分割的方法,將車牌中字符部分的像素點根據(jù)坐標間的歐氏距離聚為七類,根據(jù)神經(jīng)元權值向量得到車牌的傾斜角度估計值,從而達到傾斜校正的目的。另外經(jīng)過預處理后,SOM算法得到的權值向量還可以進一步利用最短距離法對車牌字符進行分割,實例說明了本算法的有效性。最后,在SOM競爭算法的基礎上,結合徑向小波網(wǎng)絡,提出了一種自生成圓盤細胞分裂算法,并作為前一工作的后續(xù),將其應用于車牌字符的識別應用中。該算法利用競爭機制將輸入模式映射到二維單位圓盤上而不是矩形域中的神經(jīng)元上,作為徑向小波神經(jīng)網(wǎng)絡的隱層神經(jīng)元,且不需事先給定規(guī)模,所需神經(jīng)元的個數(shù)與分布情況均可由網(wǎng)絡分類結果確定并由初始神經(jīng)元模仿細胞分裂的方式得到。其中用于賦值分裂后神經(jīng)元權值的“圓周近鄰策略”不僅可以使得輸入樣本的圓盤映射保持拓撲序,還可以有效利用已經(jīng)通過競爭算法訓練過的神經(jīng)元權值,使得算法更具高效性。通過對車牌中的英文字母樣本、英文字母或數(shù)字樣本進行分類識別實驗,相比于經(jīng)典徑向基函數(shù)(RBF)網(wǎng)絡,本文提出的算法可以在得到較小規(guī)模的網(wǎng)絡前提下達到更高的識別正確率,具有較高的實用價值。
[Abstract]:Wavelet analysis and neural network are the main components of the new generation of computing intelligent information processing technology. The wavelet transform is a local transformation of a time domain and frequency domain. Using the expansion and translation operation of the wavelet function to analyze the signal in different scales, the useful information can be extracted from the signal effectively. It overcomes the tradition. Fu Liye transform can not simultaneously carry on the defect of time frequency analysis, so it becomes one of the frontier technologies of nonlinear science. The artificial neural network is a theoretical model formed by the rational abstraction of the neural network of the human brain. It can rely on self learning ability of the environment to obtain knowledge, and use the connection weights between the neurons. The wavelet neural network (WNN) is a product of the combination of wavelet analysis and neural network. It can maintain the ability of multi input parallel processing, self learning, nonlinear mapping and fault tolerance on one hand. On the other hand, the wavelet analysis with strong mathematical basis is easily applied to the high level. On the basis of the existing classical wavelet neural network model, this paper has studied the existing classical wavelet neural network model. The former is the main research object, the initialization of the adaptive wavelet network with tensor product type wavelet function, the structure and algorithm of the fuzzy wavelet network combining with the fuzzy mechanism, the radial wavelet network. The algorithm of the collaterals has been studied and analyzed in detail, and it is applied to signal prediction, system identification, pattern classification and so on, which make the wavelet networks have higher practical value and significance in application. The main work is: first, multidimensional input adaptive wavelet with the tensor product type wavelet activation function Network (WNN_M) is the earliest and most classic wavelet network model. Its scale parameters, translation parameters and linear weights are all adjustable parameters of the network. In application, the flexibility of the network is added, making it possible to approximate the nonlinear system with a smaller scale wavelet network. In its application, the initial parameters are still more Q.H.Zhang proposed. The heuristic method (HIA). In order to get the more ideal initial parameters for the model and make the network training fast convergence, an improved parameter initialization method (CIA) is proposed using the time-frequency localization feature of the hidden layer wavelet function (CIA). The method first sets the threshold vector according to the statistical characteristics of the multidimensional input data, and uses the most important method. The idea of adjacent clustering determines the number of neurons in the hidden layer of the network, and determines the initial value of the corresponding wavelet translation reference vector and the scale parameter by the time window width of the hidden layer wavelet function. The initial error of the three different time series prediction shows that the initial error of the WNN_M-CIA is compared to the WNN_M-HIA. It is obviously lower. After several iterations of the gradient descent method, the prediction accuracy is still better than the network prediction accuracy based on HIA initialization. It shows the validity and rationality of the initialization algorithm. Secondly, considering the trend of combining the fuzzy theory with the wavelet analysis and the neural network, a kind of TSK fuzzy system and the wavelet network are proposed. This model uses the wavelet network with continuous parameter and tensor product multidimensional wavelet activation function as the conclusion part of the TSK fuzzy system, analyzes the difference between the fuzzy wavelet network model and the existing fuzzy wavelet network model, and applies it to the nonlinear system identification. In the training algorithm, the selection of the particle swarm optimization is based on the particle swarm optimization. The hybrid optimization algorithm combined with the online gradient descent method. Through the identification experiments of two nonlinear systems, it can be seen that even if less fuzzy rules and parameters are used, compared with the existing models, the proposed model and algorithm can still get more satisfactory identification results. Again, based on the competition algorithm. The Kohonen self organizing mapping (SOM) neural network has proposed a method of tilt correction and character segmentation for the license plate images. The pixels of the characters in the license plate are divided into seven classes according to the Euclidean distance between the coordinates, and the estimation of the tilt angle of the license plate is obtained according to the weight vector of the neuron. In addition, the purpose of the tilt correction is achieved. After preprocessing, the weight vector obtained by the SOM algorithm can also further use the shortest distance method to segment the character of the license plate. The example shows the effectiveness of the algorithm. Finally, on the basis of the SOM competition algorithm, a self generated circular disk cell division algorithm is proposed with the radial wavelet network, which will be used as a follow-up of the previous work. It is applied to the recognition of license plate characters. The algorithm uses competition mechanism to map the input mode to the two-dimensional unit disk instead of the neuron in the rectangular domain. As a hidden layer neuron of the radial wavelet neural network, the number and distribution of the required neurons can be classified by the network classification results. The "circumference nearest neighbor strategy", which is used to assign the weights of the neurons after the splitting, can not only make the disk mapping of the input sample keep the topological order, but also effectively use the weights that have been trained by the competition algorithm, making the algorithm more efficient. Compared with the classic radial basis function (RBF) network, the proposed algorithm can achieve higher recognition accuracy and have higher practical value than the classical radial basis function (RBF) network.
【學位授予單位】：中北大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TP183;TP391.4

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