A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

Semi-Fragile Audio Watermarking Algorithm in DWT Domain

A novel semi-fragile audio watermarking algorithm in DWT domain is proposed in this paper.This method transforms the original audio into 3-layer wavelet domain and divides approximation wavelet coefficients into many groups.Through computing mean quantization of per group,this algorithm embeds the watermark signal into the average value of the wavelet coefficients.Experimental results show that our semi-fragile audio watermarking algorithm is not only inaudible and robust against various common images processing,but also fragile to malicious modification.Especially,it can detect the tampered regions effectively.

Model identification of continuous stirred tank reactor based on QKLMS algorithm

The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.

An Improved Image Enhancement Algorithm

To solve the problems of noise,detail loss and poor contrast in the successive mean quantization transform（SMQT）,a new SMQT algorithm based on Otsu algorithm is proposed.In this algorithm,we integrate the optimal threshold selected by the Otsu algorithm into the SMQT algorithm,then obtain the successive mean quantization of the binary tree.By this algorithm,an enhanced image is output with a higher quality.From both subjective visual effect and objective quality evaluation,the experimental results show that the improved algorithm reduces noise,improves contrast and makes the image details more clear.