本文中，首先是提出一種改良式粒子群神經網路模型IPSONN(Improved Particle Swarm Optimization Neural Network )，藉由改變加速係數來平衡個體經驗及群體經驗，使得粒子在開始探索階段和後面收斂階段都能有較大的數值來提升搜尋能力，並利用非線性的特性來改善粒子群演算法易落入區域最佳解的缺點，然後用這改良式粒子群演算法來訓練神經網絡。另外提出一個以群體最佳解作為改良的粒子群神經網路模型PSOHBNN (Particle Swarm Optimization Hybrid Backpropagation Neural Network)，改善傳統粒子群演算法的缺點，讓PSO (Particle Swarm Optimization)中的粒子能多一次跳脫區域解的機會，找到全域最佳解的位置，最後我們再將這兩方法做結合，命名為IPSOHBNN (Improved Particle Swarm Optimization Hybrid Backpropagation Neural Network) 神經網路模型。我們再將這三種演算法做為訓練前饋神經網路的學習算法來對多模態函數進行函數的適應。經由模擬的結果顯示，本文所提出的改良後的粒子群演算法在訓練神經網路時對大部份的函數都有良好的預測效果，最後對空氣品質汙染指標(PM2.5)的濃度進行預測，而從預測數據的圖表中得知本文所提出的改良後的粒子群演算法，能有效地訓練出良好的網路模型並準確地預測出PM2.5的濃度。

In this thesis, first propose an Improved Particle Swarm Optimization Neural Network model (IPSONN), by changing the acceleration coefficient to balance the personal and social experience, let particles at the beginning and the end of the searching stage have bigger value to enhance the searching ability, also use the nonlinear characteristics to improve the disadvantage of particle swarm algorithm which easily fall into the local optimum, then use improved PSO algorithm to train neural network. In addition, propose (PSOHBNN) model which is improved based on social experience, make particles have chance to jump out of the valley and find the global optimum. Then, we combine these two method, named Improved Particle Swarm Optimization Hybrid Backpropagation Neural Network model (IPSOHBNN), take these three algorithms as the learning algorithm for training feedforward neural network and do the function approximation for benchmark functions. In the results, the proposed PSO algorithms in training neural network have good prediction value for most of functions. Finally, these models applied to forecast the concentration of air quality pollution index (PM2.5), from the figure of test data can see the proposed PSO algorithms effectively train good network model and forecast the concentration of PM2.5 accurately.

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