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課程簡介 Course Introduction
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開課年度學期 Year / Term
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113 學年度 第 1 學期
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開課班級 Department
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電機工程學系碩士班 電機系碩一二合
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授課方式 Instructional Method
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課堂教學 、 中文
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課程電腦代號 Course Reference Number
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182008
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課程名稱(中文) Course Title(Chinese)
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仿生最佳化演算法
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課程名稱(英文) Course Title(English)
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Bionic Optimization Algorithm
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學分數/時數 Credit Hours
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3 /
3
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必(選)修 Requirement / Elective Course
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選修
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授課老師 Instructor
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黃崇能
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助教 Teaching Assistant
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上課時間 Meeting Time
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星期一，節次2、3、4
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上課教室 Classroom
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ZB209
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Office Hours
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| 獲獎及補助情形 Awards and Grants |
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| 聯合國永續發展目標 (SDGs跨域類別) Sustainable Development Goals, SDGs |
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SDGs 07.
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可負擔的潔淨能源：確保所有的人都可取得負擔得起、可靠、永續及現代的能源
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SDGs 09.
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工業化、創新及基礎建設：建立具有韌性的基礎建設，促進包容且永續的工業，並加速創新
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課程目標 Learning Objectives
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Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researchers in recent years and outperforming other fuzzy inference systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks while solving various problems in the field of business and finance.
This course aim at dealing with the issues such as prone slow and prone to get stuck in local minima in learning algorithm. Besides, students can learn how to integrate the modern bionic optimizations with artifical intelligent methods while serving the "black box" problems in academic science or engineerings.
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先修 ( 前置 ) 課程 Prerequisite
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Fuzzy Neural Networks (FNNs),Artificial Neural Networks (ANNs),Adaptive Neuro-Fuzzy Inference System (ANFIS), C and Matlab algorithms
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| 彈性教學規劃 Flexible Teaching/Planning Schedules |
| ＊本課程實施16+2週彈性教學方案，其中第17、18週之彈性規劃如下： |
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課程大綱 Course Syllabus
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| 週次 Week |
課程單元大綱 Unit |
教學方式 Instructional Method/Style/Teaching Style |
參考資料或相關作業 References or Related Materials |
評量方式 Grading |
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1
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Concept of ANFIS
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ppt
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2
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Training Methods
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ppt
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3
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Applications in Engineerings
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ppt
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4
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metaheuristic algorithms
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ppt
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5
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genetic algorithm
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ppt
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6
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Particle Swarm Optimization
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ppt
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7
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middle reports
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8
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Artificial bee colony algorithm
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ppt
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9
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Cat Swarm Optimization Algorithm
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ppt
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10
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Simulated annealing
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ppt
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11
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Ant colony optimization
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ppt
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12
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Harmony search
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ppt
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13
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Bat algorithm
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ppt
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14
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Final reports
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單一課程對應校能力指標程度 The Degree to Which Single Course Corresponds to School Competence
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| 編號 No. |
校核心能力 School Core Competencies |
符合程度 Degree of conformity |
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1
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公民力 (Citizen)
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4
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2
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自學力 (Self-learning)
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5
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3
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資訊力 (Information)
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5
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4
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創造力 (Creativity)
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5
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5
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溝通力 (Communication)
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5
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6
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就業力(Employability)
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5
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單一課程對應系能力指標程度 The Degree to Which Single Course Corresponds to Department Competence
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| 編號 No. |
類別 Category |
系核心能力 Department Core Competencies |
符合程度 Degree of conformity |
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01
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系所
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具備專業知識運作及運用之能力
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5
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02
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系所
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發掘問題、實驗分析及驗證之能力
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5
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03
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系所
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創新思考開發之能力
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5
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04
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系所
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協調合作、領導團隊與管理規劃之能力
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5
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05
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系所
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中英文寫作與簡報之能力
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5
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06
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系所
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強化國際觀與國際交流之能力
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5
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單一課程對應院能力指標程度 The Degree to Which Single Course Corresponds to College Competence
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| 編號 No. |
院核心能力 College Core Competencies |
符合程度 Degree of conformity |
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1
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語文能力
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5
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2
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溝通與合作能力
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5
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3
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創新與實踐能力
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5
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4
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專業知能
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5
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教科書或參考用書 Textbooks or Reference Books
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館藏書名 Library Books
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備註 Remarks
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Metaheuristics - SpringerLink ISBN: 978-0-387-71921-4
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※請尊重智慧財產權，不得非法影印教科書※
※ Please respect intellectual property rights and do not illegally photocopy textbooks. ※
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教學方法 Teaching Method
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教學方法 Teaching Method
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百分比 Percentage
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講述
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60 %
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個案研討
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20 %
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問題導向學習
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10 %
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專題實作
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10 %
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| 總和 Total |
100 % |
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成績評量方式 Grading
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| 評量方式 Grading |
百分比 Percentage |
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小組口頭報告
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30 %
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課堂參與
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20 %
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作業撰寫
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40 %
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出席狀況
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10 %
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| 總和 Total |
100 % |
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課程大綱補充資料 Supplementary Material of Course Syllabus
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