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摘要:股票市場在國家經(jīng)濟(jì)中占據(jù)著極其重要的地位，對股票市場的分析與預(yù)測具有重要理論意義和實(shí)際應(yīng)用價(jià)值。但由于股票市場受經(jīng)濟(jì)等多方面因素的影響，導(dǎo)致傳統(tǒng)方法效果不甚理想。人工神經(jīng)網(wǎng)絡(luò)作為一門非線性科學(xué)，由于其自身很強(qiáng)的容錯(cuò)性、自適應(yīng)性和非線性映射能力，為股票市場的建模和預(yù)測提供了新的技術(shù)與方法，其中應(yīng)用最為廣泛的是按誤差逆?zhèn)鞑ニ惴ㄓ?xùn)練的多層前饋網(wǎng)絡(luò)BP神經(jīng)網(wǎng)絡(luò)。但BP神經(jīng)網(wǎng)絡(luò)存在學(xué)習(xí)速度慢、易陷入局部極小值、權(quán)值、閾值及網(wǎng)絡(luò)結(jié)構(gòu)的選擇具有很大的隨機(jī)性、預(yù)測結(jié)果精度不高等缺點(diǎn)。 本文首先編程實(shí)現(xiàn)了基于粒子群算法的改進(jìn)遺傳算法，并通過六種典型函數(shù)測試，驗(yàn)證了編程的準(zhǔn)確性及改進(jìn)遺傳算法在提高算法穩(wěn)定性和收斂精度方面的有效性。以上證綜合指數(shù)為研究對象，在通過仿真實(shí)驗(yàn)確定了BP神經(jīng)網(wǎng)絡(luò)隱層單元數(shù)目的基礎(chǔ)上，利用改進(jìn)遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)權(quán)值與閾值，建立了BP神經(jīng)網(wǎng)絡(luò)基本數(shù)據(jù)預(yù)測模型和技術(shù)指標(biāo)預(yù)測模型。仿真實(shí)驗(yàn)結(jié)果表明，BP神經(jīng)網(wǎng)絡(luò)應(yīng)用于上證綜合指數(shù)預(yù)測是可行的，而基于改進(jìn)遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)能有效提高預(yù)測精度。 關(guān)鍵詞 預(yù)測；BP神經(jīng)網(wǎng)絡(luò)；粒子群算法；遺傳算法；上證綜合指數(shù)
Abstract:The stock market plays an important role in chinese economy, and the analysis and prediction of the stock market has the important theoretical significance and practical application value. As the stock market is influenced by various factors such as economy, the traditional forecasting methods effect is not very ideal. Artificial neural network as a nonlinear science, because of its strong fault tolerance, adaptability and nonlinear mapping ability, provides a new technology and method for the modeling and forecasting of the stock market. One of the most widely used mehod is the BP neural network, which according to the error back propagation algorithm training of the multilayer feedforward network. But the BP neural network has many shortpionts,such as it’s learning speed is slow, and it is easy to fall into local minimum value, weights, thresholds, and it has great randomness about the choice of network structure, the prediction precision of faults and so on. Firstly the programme realized the improved genetic algorithm based on particle swarm optimization (pso) algorithm, and through six kinds of typical function test, verify the accuracy of the programming and improved genetic algorithm to improve the effectiveness of the stability and precision of convergence. With the Shanghai composite index as the research object, through the simulation experiments confirmed the BP neural network hidden layer unit number, on the basis of improved genetic algorithm was used to optimize the BP neural network weights and threshold value, the basic data of BP neural network prediction model and the technical index prediction model are established. Simulation experimental results show that the BP neural network is applied in the Shanghai composite index prediction is feasible, and based on improved genetic algorithm to optimize the BP neural network can effectively improve the prediction precision. Keywords Prediction BP neural network PSO optimization Genetic algorithm Shanghai composite index |
