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Machine Learning in Engineering Problems

Machine Learning in Engineering Problems. Jzau-Shenlg Lin ( 林灶生 ) Dept. of Computer Science and Information Engineering, Nat’l Chin-Yi Institute of Technology. Outline. Introduction Artificial Neural Networks (ANN) Fuzzy-, Possibilistic-, and Rough- Systems

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Machine Learning in Engineering Problems

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  1. Machine Learning in Engineering Problems Jzau-Shenlg Lin (林灶生) Dept. of Computer Science and Information Engineering, Nat’l Chin-Yi Institute of Technology

  2. Outline • Introduction • Artificial Neural Networks (ANN) • Fuzzy-, Possibilistic-, and Rough- Systems • Cerebellar Model Arithmetic Computer (CMAC) • Genetic Algorithm (GA) • Artificial Immune System (AIS) • Ant Colony System (ACS) • Support Vector Machine (SVM) • Conclusions

  3. Introduction(1/2) • Machine learning is a research strategy, in which computers can modeling or implement the humans’ learning behaviors. • It also reconstructs the intelligent architecture in its intelligent base to reinforce the performance for itself. • H.A. Simon indicated that learning is an adaptive activity for a system to causes the system doing the same or similar task more effectively. • R.S. Michalski thought that learning is the representation to configure or revise the experimental tasks. • The experts who design expert systems presented that learning is extracting intelligence.

  4. Introduction(2/2) • Learning is a very important feature for the intelligent behavior. • The applications for the machine learning include: • Robots • Computer game • Signal processing – Compressing, Recognition, watermarking, … • Network topology – The shortest path, Channel assignment, … • Several optimization problems in engineering

  5. Synapse(神經連接線) --依電位變化傳遞 Dendrites(神經樹) --輸入路徑 Axon(神經軸)—輸出路徑 Soma(神經核)—細胞本體 Neurobiological model 1.Artificial Neural Networks (ANN) (1/6) • An ANN is a Massively parallel distributed Processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: (a) Knowledge is acquired by the network through a learning process. (b) Interneuron connection strengths known as synaptic weights are used to store the knowledge. • Neural Networks are referred to in the literature asneurocomputers, connectionist networks, parallel distributed processors,etc.

  6. Bias input Activation function Output : : Summing junction Threshold Synaptic Weights 1. Artificial Neural Networks (ANN) (2/6) • Models of a Neuron

  7. 1. Artificial Neural Networks (ANN) (3/6) • Type of Activation Function (a) Threshold Function (b) Piecewise-linear Function (c) Sigmoid Function Slope=a

  8. Input layer of source nodes Output layer of neurons Input layer of source nodes Layer of hidden neurons Output layer of neurons 1. Artificial Neural Networks (ANN) (4/6) • Feedforward Architecture

  9. Z-1 Z-1 Z-1 Z-1 1. Artificial Neural Networks (ANN) (5/6) Input layer of source nodes • Recurrent and 2-D Lattice Networks

  10. Neural Networks Optimal Nets ●Hopfield-Tank net ● Annealing net ● Bolzmann machine Fixed Nets ● Hamming net ● Hopfield net ● Bi-direction associative memory Unsupervised Nets ●Self- organization map ● ART ● Neocognitron ● Competitive learning ● Principle component analysis (PCA) ● Independent component analysis (ICA) Supervised Nets ● Perceptron ● Back-Propagation delay ● Probabilistic net ● Multilayer Perceptron ● ADALINE ● LVQ ● Counter propagation net 1. Artificial Neural Networks (ANN) (6/6) • Classifications

  11. 2.1 Fuzzy-Systems • Fuzzy C-Means (FCM) Hard-C-Means (HCM)Fuzzy-C-Means (FCM)

  12. 2.1 Fuzzy-Systems • Penalized FCM and Compensated FCM Penalized FCM (PFCM)Compensated FCM (CFCM)

  13. 2.1 Fuzzy-Systems • The curves of ln(i)and tanh (-i)within 0  i  1

  14. x3 x12 x15 x6 x8 x2 x5 x11 x14 x7 x9 x4 x10 x13 x1 2.1 Fuzzy-Systems • Fuzzification in the training example --Butterfly

  15. 2.2 Possibilistic-System • Possibilistic C-Means (PCM) – Proposed by Krishnapuram and Keller : Scale parameter at the i-th cluster. : Possibilistic typicality value of training sample belonging to the i-th cluster.

  16. 2.3 Fuzzy-, Possibilistic-Systems(1/7) • Fuzzy Possibilistic C-Means (FPCM) -- Proposed by Pal, Pal, and Bezdek

  17. Cluster Cluster Training sample 1 2 ….. c 1 2 ….. c z1 z2 Typicality function zn Membership function 2.3 Fuzzy-, Possibilistic-Systems(2/7) • FPCM-- Membership and Typicality

  18. x12 x8 x2 x1 x3 x5 x7 x9 x11 x6 x4 x10 2.3 Fuzzy-, Possibilistic-Systems(3/7) • Simulated data set

  19. 2.3 Fuzzy-, Possibilistic-Systems(4/7) • Penalized FPCM =JFPCM - scale factors based on clusters and training samples

  20. 2.3 Fuzzy-, Possibilistic-Systems(5/7) • Compensated FPCM =JFPCM + scale factors based on clusters and training samples

  21. Codebook PFPCM/ CFPCM DCT AC coefficients DC coefficient Original Image Encoder DC + Index Transmission DC + Index IDCT DC coefficient Codebook AC coefficients Decoder Reconstructed Image 2.3 Fuzzy-, Possibilistic-Systems(6/7)

  22. Original Image LBG DCT + LBG(VQ) DCT + CFPCM(VQ) 2.3 Fuzzy-, Possibilistic-Systems(7/7)

  23. 2.4 Rough- System (1/5) • Rough set • Let R be a binary equivalence relation defined on a universal set Z is a subset of the Cartesian product, . • An equivalence relation is a binary relation, R, that satisfies • R is reflexive : • R is symmetric : • R is transitive : • can be defined as the union of all equivalence classes in Z/R that are contained in A such that • can be also defined as the union of all equivalence classes in Z/R that overlap with A like the following equation

  24. 2.4 Rough- System (2/5) • Rough set • A rough set can be represented by and with the given set A as • And the rough boundary of A by the equivalence classes Z/R is distinct as • Interconnection models in the architecture of rough neurons (a) Fully connected (b) Exciting model (c) Inhibiting model

  25. 2.4 Rough- System (3/5) • Rough Neurons (Proposed by Lingras in 1998) • Definition for the Exciting model in the rough neurons

  26. 2.4 Rough- System (4/5) • Rough Fuzzy Hopfield Neural Network (RFHNN) Netx,i : Netx,i :

  27. Morphology Processing Result 2.4 Rough- System (5/5) • Processing to the Multi-Spectral Image using RFHNN

  28. 2.5 Fuzzy-, Possibilistic-, Rough- Systems + Artificial Neural networks • Fuzzy Competitive Learning Network (FCLN) • Penalized Fuzzy Competitive Learning Network (PFCLN) • Compensated Fuzzy Competitive Learning Network (CFCLN) • Rough Fuzzy Competitive Learning Network (RFCLN) • Fuzzy Hopfield Neural Network (FHNN) • Penalized Fuzzy Hopfield Neural Network (PFHNN) • Compensated Fuzzy Hopfield Neural Network (CFHNN) • Fuzzy-Possibilistic Hopfield Neural Network (FPHNN) • Rough Fuzzy Hopfield Neural Network (RFHNN)

  29. 3. Cerebellar Model Arithmetic Computer (CMAC) (1/4) • CMAC, named Cerebella Model Articulation Controller, was proposed by J.S. Albus in1975. • CMAC is a model of associate memory network. • In the training phase, the CMAC updates the weights in memory by using a transformation from input samples. • It can easily obtain the outputs by looking up the weights in memory in accordance with the input vectors in the recognition phase. • Due to a simple manner with memory architecture, the CMAC can be easily implemented into hardware circuit.

  30. a w a1 a2 a4 a5 a6 a7 aN w1 w2 w4 w5 w6 w7 wN y Input Vector 3. Cerebellar Model Arithmetic Computer (CMAC) (2/4) • Traditional CMAC Architecture

  31. 1 m94 m2 . . . a0 ~ a15 . . . m1 2 . . . coding 1 Sum 94 3 4 2 Class 1 m94 5 m2 m1 . . . a0 ~ a15 . . . 6 . . . coding 7 Input signals with quantizing binary code 8 Class 2 m94 61 m2 Sum 2 m1 . . . a0 ~ a15 . . . 62 N . . . coding Sum 1 63 64 Input Pattern Class 16 Output Weights Memory 3. Cerebellar Model Arithmetic Computer (CMAC) (3/4) • Modified CMAC Architecture with Clustering Memory

  32. 3. Cerebellar Model Arithmetic Computer (CMAC) (4/4) • Applied CMAC to Character Recognition 8 error pixels in characters 14 error pixels in characters 18 error pixels in characters

  33. 4. Genetic Algorithm (GA)(1/4) • Evolutionary computing was introduced in the 1960s by I. Rechenberg in his work "Evolution strategies" (Evolutions strategie in original). • His idea was then developed by other researchers. Genetic Algorithms (GAs) were invented byJohn Hollandand developed by him and his students and colleagues at University of Michigan, 1970’s . • Directed search algorithms based on the mechanics of biological evolution and Survival with a fitness function. • Functions of GA: • Chromosome • string of DNA • consists of genes • a solution of the problem • Fitness • measure the chromosome • survival or not • Reproduction • crossover • two chromosomes • combine the genes from parents • form new chromosomes • mutation • occurs on single chromosome • elements of DNA are a bit changed

  34. 4. Genetic Algorithm (GA)(2/4) Simple Genetic Algorithm() { Randomly initialize population; evaluate population; while(termination criterion not reached) { select solutions for next population with a fitness function (reproduction); perform crossover and mutation; evaluate population (to produce new offspring); } }    

  35. 4. Genetic Algorithm (GA)(3/4) Population New Offspring mutation crossover Evaluate Roulette-Wheel Selection Reproduction Fitness Function Evolution Circumstance Evolutionary Procedure

  36. 4. Genetic Algorithm (GA)(4/4) • Combing GA with other systems • GA + Fuzzy Algorithm • GA + Possibilistic Algorithm • GA + Rough algorithm • GA + Artificial Neural Network

  37. 5. Artificial Immune System (AIS)(1/9) • The AIS transfers the characteristics of natural immune system with mathematic model into computing system in algorithm manner to solve the engineering problems. • The AIS is based on Jerne’s idiotypic network theory (Jerne, 1973), which suggests that the immune system maintains a network of interconnected B-cells.

  38. 細胞及分泌物 吞噬細胞 淋巴細胞 補體 B細胞及抗體 T細胞及淋巴球 5. Artificial Immune System (AIS)(2/9) • Natural immune system in human body 自然殺手細胞

  39. 5. Artificial Immune System (AIS)(3/9) • The structure of multi-protection and -defense system in immune system

  40. 5. Artificial Immune System (AIS)(4/9) • Models in AIS 1.Antibody Network 2.Evolutionary Algorithm • Immune Genetic Algorithm (IGA) • Immune Evolutionary Programming (IEP) • Immune Evolutionary Strategy (IES) 3.Colonel Selection Principle (CSP)

  41. 抗原激發 抗體群 Ab population 選擇抗體 Ab Selection 相似成熟度 Affinity Maturation Selection 重選抗體 Ab Re-selection 激發細胞 非激發細胞 繁殖Clone 衰亡 Death 5. Artificial Immune System (AIS)(5/9) • Antibody Network (1)

  42. Start Immune System Input external Ags B-Cell’s surface Character of Ags displayed by B-cell Th cells secrete to start immune reaction Activate lymphoid cells All Ags are removed? N Generate Abs Continuously? N Y Stop immune reaction Y Activate Ts to secrete IL¯ Inhibit immune reaction Generate Abs 5. Artificial Immune System (AIS)(6/9) • Antibody Network (2)

  43. 抗原與目的函數、制約條件的對應 記憶親和度(R1) 高的抗體 促進和抑制 生成起始抗體群 生成新抗體 抗體與抗原親和度(R1)計算 抗體與記憶親和度(R2) 的計算 排除適量親和度(R2) 高的抗體 親和度(R1)=1? N Y 抗原排除 (結束) 5. Artificial Immune System (AIS)(7/9) • Immune Genetic Algorithm with Constraint (IGAC)

  44. 啟動 族群更新及適應因子計算 最佳族群? Y 疫苗注射 停止 N 免疫選擇 選擇、交配、及突變 5. Artificial Immune System (AIS)(8/9) • Immune Genetic Algorithm with Vaccination (IGAV)

  45. 5. Artificial Immune System (AIS)(9/9) • Antibody Network (2) + Fuzzy Algorithm to Image segmentation

  46. 6. Ant Colony System (ACS)(1/4) • Ant system algorithm, based on behavior of real ants, is a natural approach to establish from their nest to food source. • An ant moves randomly and detects a previously laid pheromone on a path in order to find the shortest way between their nest and the food source. • Ant system algorithm is an important methodology to apply on non-linear optimal problems recently. • It is a parallel architecture to force ants move simultaneously, independently, and without supervisor.

  47. 6. Ant Colony System (ACS)(2/4) • Each ant chooses the next position to visit in accordance with the visibility of the position and the pheromone intensity. • The k-th ant starting from position i decides to visit position j with the probability defined as follows: where is the visibility of position j from position i, and are two heuristically defined parameters.

  48. 6. Ant Colony System (ACS)(3/4) • We define the pheromone intensity on path (i, j) at time t to be and to assign a random value to it when t = 0. • Along the path from i to j, a trail substance is laid on path (i, j) and defined as: where Q is a constant and is the tour length of the k-th ant.

  49. 6. Ant Colony System (ACS)(4/4) • When the ant has completed a position and a cycle of n iterations is consisted, the laid trail substance is used to update the amount of substance previously laid as the following equation: and where is a coefficient of persistence of the tail and is the quantity of trail substance laid on path (i, j) by the k-th ant during a cycle( between time t and t + n).

  50. 6.1 Annealing algorithm + ACS (AACS) • In the scheme of ant system algorithm, the total cost function for the network topology from node i to k and cooling schedule can be defined as where • The probability that the k-th ant starting from node i to visit node j undergo random thermal perturbations at a given temperature T conforms to a Boltzmann distribution and

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