在這篇論文中，我們提出了利用粒子群優化來解決雜訊影像和預測問題。
首先，提出了一種新的排序型開關中值濾波器（SSMF）可以來針對極端損壞的影像做去除雜訊，同時亦可保留影像細節的部分。在檢測階段，中心像素點將會被辨識出是“未損壞”或“損壞”的像素點。排序型開關中值濾波器在處理損壞的像素點時，周圍擁有較多的未損壞像素點者，將在過濾階段擁有更高的優先濾波處理。在五種影像雜訊模型及其實驗下，所提出的SSMF的性能，能有效的去除高雜訊密度（雜訊密度範圍從10％到90％）的灰階和彩色影像，實驗證實我們所提出的SSMF大大優於其他現有的中值濾波器。
其次，我們提出了自適應影像使用非區域方法並配合動盪的粒子群優化（TPSO）基於一個沒有參考影像的度量Q。在大多數實際情況，即使在沒有提供“無雜訊”的參考影像下，濾波器亦可處理雜訊影像。在本文中，我們結合TPSO和NLM以提出TPNLM的過濾器植基於度量Q。所提出的過濾器能夠在不需要任何相關之參考影像下去除高斯雜訊，同時保持精細的影像細節，邊緣和紋理。實驗在幾個加了未知情況的高斯雜訊影像模擬下，證明所提出的方法在去除雜訊的性能表現，無論是在視覺、峰值訊噪比（PSNR）和結構相似性指數（SSIM）等數據下都比現有的方法還要好。
第三，提出了修改動盪粒子群優化（MTPSO）方法用於溫度預測和台灣期貨交易指數（TAIFEX）預測，植基於兩個因素的模糊時間序列和粒子群優化。MTPSO模型可以更容易且準確地處理模糊時間序列的時間間隔長度和內容預測規則等兩個主要影響因素。溫度預測及期貨指數預測實驗結果顯示，所提出之模型是優於任何現有的模型，它可以在高階模糊時間序列下得到更優質的解決方案。
最後，新的基金趨勢及交易策略模型結合動盪粒子群優化（TPSO）和均線技術指標用來尋找適當的技術指標參數，以實現高利潤低風險的共同基金操作。所提出的方法利用移動平均線的時間間隔和交易模型來提供了幾個很好的基金交易買入和賣出點以減少損失。實驗結果說明，該模型的性能比原有的基金效能更好。

In this dissertation, noise image filters and Prediction problems based on particle swarm optimization are proposed.
Firstly, a novel sorted switching median filter (i.e. SSMF) for effectively denoising extremely corrupted images while preserving the image details is proposed. The center pixel is considered as “uncorrupted” or “corrupted” noise in the detecting stage. The corrupted pixels that possess more noise-free surroundings will have higher processing priority in the SSMF sorting and filtering stages to rescue the heavily noisy neighbors. Five noise models are considered to assess the performance of the proposed SSMF algorithm. Several extensive simulation results conducted on both grayscale and color images with a wide range (from 10% to 90%) of noise corruption clearly show that the proposed SSMF substantially outperforms all other existing median-based filters.
Secondly, an adaptive image denoising algorithm that uses the non-local means in conjunction with the turbulent particle swarm optimization (i.e. TPSO) which based on a no-reference metric Q is presented. The proposed noise image filter can deal with the noisy image even if no “noise-free” reference is available in most practically circumstances. We combined TPSO and NLM to propose the TPNLM filter. The proposed filter is able to denoising Gaussian noise without need for any knowledge about the noise-free image, at the same time preserving fine image details, edges and textures well. We also demonstrate several simulations with images contaminated by additive Gaussian noise under unknown noise variance to show that the performance of the proposed method surpasses those of previously published works, both in visual and in terms of peak signal to noise ratio (PSNR) and the structural similarity index (SSIM), respectively.
Thirdly, a modified turbulent particle swarm optimization (named MTPSO) method is proposed for the temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on the two-factor fuzzy time series and particle swarm optimization. The MTPSO model can be dealt with two main factors easily and accurately, which are the lengths of intervals and the content of forecast rules. The experiment results of the temperature prediction and the TAIFEX forecasting show that the proposed model is better than any existing models and it can get better quality solutions based on the high-order fuzzy time series, respectively.
Finally, a new funds trading strategy that combines turbulent particle swarm optimization (named TPSO) and mixed moving average techniques is presented and used to find the proper content of technical indicators’ parameters to achieve high profit and low risk on the mutual funds. The time interval of moving average of the proposed method is adjustable and the trading model could avoid and reduce loss by providing several good buying and selling points. The experiment results show that the performance of the proposed model is better than the best original performance.

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