本論文充分利用智慧型最佳化演算法之優勢，將其應用於通訊陣列之上，並進而對通訊陣列間之耦合效應進行探討與補償。依據通訊陣列之特性，先針對通訊陣列的分析與設計進行研究，探討改變波束函數中的陣列元素參數，對陣列場形圖所產生之影響，然後再進一步探討適應性陣列，包含其最佳化與耦合效應。本論文所闡述之理論與方法可適用於所有通訊陣列，包括空中電磁波天線陣列與水下聲波陣列。
本論文首先針對通訊陣列進行解析，而後再援引入智慧型最佳化演算法，並透過不同的元素參數調整方式，對通訊陣列之場形圖進行最佳化設計。所採用之智慧型最佳化演算法有粒子群演算法(particle swarm optimization, PSO)、類電磁演算法(electromagnetism-like algorithm, EM-like)及蛙跳演算法(shuffled frog-leaping algorithm, SFLA)三種，此三種演算法的優點在於求解快速，且尋優過程中皆毋須使用梯度運算，可降低落入區域解之可能性。另外，此三種演算法之公式皆十分簡單，易於理解與程序規劃，且無須編碼，可直接在十進制中進行操作。陣列最佳化設計旨在提高通訊系統之品質，其可藉由調整陣列元素之位置、振幅及相位來達成。本論文所採用之方式，乃是基於主波束寬度或零值(null)方向的要求下，盡可能地抑制旁波瓣級(sidelobe level, SLL)。透過各章之模擬結果可知，本論文所採用之智慧型算法，可自動尋找全域解，降低落入區域解之可能性，對於求解陣列最佳化十分有利。本論文更進一步針對適應性陣列進行探討，如眾所周知，傳統適應性陣列的分析方式多半採用最小均方法(least mean square, LMS)、遞迴最小平方法(recursive least squares, RLS)或廣義特徵值法(generalized eigenvalue, GE)等，這些方法皆含有梯度運算，亦即所求得之結果極易陷入區域解，是以本論文改採智慧型最佳化演算法，對適應性陣列進行分析，所求得之結果皆能滿足預設之要求。此外，通訊陣列收發訊號時，陣列元素間會產生交互影響而改變通訊品質，此效應於天線陣列中已有諸多探討，本論文乃針對水下通訊陣列之耦合效應進行探討，並提出補償之方法。
本論文共分八章。第一章闡述研究背景、動機、目的以及其重要性，在對歷史進行回顧後，更進一步說明與本論文密切相關的基礎知識。第二章，探討傳統的解析方式，即知名的切比雪夫多項式(Chebyshev polynomial)，並提出嶄新的概念，在切比雪夫空間中直接對切比雪夫陣列進行零值控制(nulling)。第三章至第五章則運用智慧型演算法，用以進行陣列最佳化設計。第三章使用粒子群演算法，透過調整位置與權重，對場形圖之旁波瓣級進行抑制。第四章採用類電磁演算法，透過調整位置、振幅與相位三種方式，對旁波瓣級進行抑制，並以相位調整的方式進行零值操控。第五章則使用蛙跳演算法對陣列場形圖進行最佳化，其最佳化目標除了抑制旁波瓣級之外，更採用束寬限制條件(beamwidth constraint, BWC)，並在此限制下進行旁波瓣級之抑制，以及達到零值方向之操控。第六章應用粒子群演算法，求解適應性陣列之最佳權重，並與最小均方法做比較。第七章則針對通訊陣列之耦合效應進行探討，並提出校正補償方法。第八章則為本文結論。

In this dissertation, the stochastic evolutionary techniques are applied to analysis and design of communication arrays. These techniques include the particle swarm optimization, electromagnetism-like algorithm, and shuffled frog-leaping algorithm. The goal is to achieve optimum performance of a communication array by adjusting the positions, amplitudes, and phases of array elements. Restrictions including beamwidth and nulling constraints can be easily imposed on our optimization processes. With the use of stochastic evolutionary techniques, proposed processes require no gradient operation, and can automatically converge toward the global solution. In addition, the mutual coupling effects are also treated in this study.
The benefits of stochastic evolutionary techniques are utilized to treat different types of communication arrays. Initially, the array perforamance is optimized by different stochastic evolutionary techniques. We utilize the particle swarm optimization, electromagnetism-like algorithm, and shuffled frog-leaping algorithm to achieve the array optimization. Note that our optimization processes require neither gradient operations nor initial guess. Therefore, our optimization processes will not get stuck in local solutions. In addition, our approaches are very easy in formulation and programming. Our analyses are further extended to treat adaptive communication arrays. Finally, the mutual coupling effects of adaptive arrays are studies in this dissertation.
This dissertation includes 8 chapters. Chapter 1 explains the background, motivation, purpose, and importance of this research. Chapter 2 gives analyses of Chebyshev array nulling in Chebyshev domain. From Chapter 3 to Chapter 5, optimizations of communication arrays are accomplished through different stochastic evolutionary techniques. In Chapter 3, the particle swarm optimization is applied to the sidelobe level minimization of array pattern. The optimization process is accomplished by adjusting the position and weight of array elements. In Chapter 4, the electromagnetism-like algorithm is applied to the synthesis of communication arrays. The goal is to minimize the sidelobe level under some constraints. The constraints include beamwidth and nulling. The adjusting methods include position-only, amplitude-only, and phase-only approaches. In Chapter 5, the shuffled frog-leaping algorithm is applied to array pattern optimization of sidelobe level reduction and nulling under the beamwidth constraint. Chapter 6 utilizes the particle swarm optimization algorithm to calculate the optimum weights of an adaptive array. Chapter 7 gives mutual-coupling analyses for the performance of adaptive arrays. Finally, the conclusion is given in Chapter 8.

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