Application And Research Of Integration Technology Based On GEP And HMM

With the current rapid development of smart technology, all kinds of intelligent methods in machine learning, natural language understanding, automatic control and other fields have been widely used. Hidden Markov Model, as a statistical model, in human-computer interaction, machine learning, speech recognition and other areas was a major breakthrough.Evolutionary algorithm as a new family of gene expression programming in genetic algorithms and genetic programming was put forward basis, which combines genetic algorithms and genetic programming design advantages, and overcome their deficiencies, genetic algorithms and genetic continuation and development of programming, was found in the function, parameter optimization, mathematical modeling and other methods than the other evolutionary algorithm has obvious advantages, has become an international research focus in the field of evolutionary computation. HMM (Hidden Markov Model, HMM) is a statistical model with learning ability, in many areas of voice and information processing has been applied successfully, the HMM parameter set is the key to the success of applications, Therefore, vector feature extraction and identification of HMM state output probability density function of the form is an important part of parameter settings, the traditional HMM-Baum-Welch algorithm is essentially a gradient descent algorithm is the advantage of faster convergence, but the parameter estimates very easy to fall into local optimum and thus affect the final results. For these reasons, consider the advantages of both HMM and GEP combines in-depth study of the latest speech recognition algorithm applied to the areas of modeling in order to achieve better results. Global search of Gene Expression Programming (Gene Expression Programming, GEP) A key feature is the ability to efficiently find the global optimal solution quickly, this paper, the GEP is introduced into the training of HMM to put forward an improved training methods (GEP -PO-BW-based HMM Algorithms, GBHA), finally realized by Matlab simulation under the above-mentioned algorithm, finally got a better method to improve the system efficiency and stability. In this paper, HMM training algorithms and modeling methods of GEP, the main research results are as follows:(1) of the HMM training algorithm is the basic theory and gives a formal definition and the basis of these definitions, the three algorithms on HMM specific analysis, and with the dynamic time warping (DWT) technology, artificial neural network (Artificial Neural Network, ANN) were compared, analysis of common strengths and weaknesses of HMM algorithm, and made their views.(2) Candida Ferreira method based on the GEP-PO algorithm used to optimize the parameters of the chromosome structure is given a formal definition, systems analysis of how to optimize the algorithm parameters, and the advantages and disadvantages of this method were analyzed. Proposed and implemented an efficient optimization of HMM training algorithm GEP (GEP-PO-BW-based HMM Algorithms, GBHA).(4) the existence of the evolutionary algorithm in learning with a network of feedback led to less than slower, the GEP proposes a HMM-based modeling algorithm (Time Series Prediction with GEP Based on HMM, H-GEPTSP).(5) integration of the experiment verify the efficiency of various algorithms and achieved the expected results.