核反应堆热工水力研究室西安交通大学NueNuclearThermal-hydraulic LaboratoryXIANJIAOTONGUNIVERSITYANN Applications for ThermalHydraulic Analysis in NuclearEngineering1896
核 反 应 堆 热 工 水 力 研 究 室 Nuclear Thermal-hydraulic Laboratory ANN Applications for Thermal Hydraulic Analysis in Nuclear Engineering
Contents 1 ANN Introductions 2 Applications in T/H Problems 3 Relevant Studies in NuTHel 4 Outlook of ANN in Nuclear Engineering 1896
CONTENTS Contents Applications in T/H Problems Relevant Studies in NuTHel Outlook of ANN in Nuclear Engineering 3 1 ANN Introductions 2 4
西安交通大学XIANJIAOTONG UNIVERSITY01ANN Introductions1896
ANN Introductions
T1.1 What is ANN ?PARTAIML&ANNArtificialIntelligenceMachineLearningArtificialNeuralNetworksMachine Learning:an approach to achieve Artificial IntelligenceANN(Artificial NeuralNetwork):a basic technicto implement MachineLearning西步交通大学
4 1.1 What is ANN ? PART 1 Artificial Intelligence Machine Learning Artificial Neural Networks ⚫ Machine Learning: an approach to achieve Artificial Intelligence ⚫ ANN(Artificial Neural Network): a basic technic to implement Machine Learning AI, ML & ANN
T1.1 Whatis ANN ?PARTArtificialneuralmodelnetworkmathematicalISorcomputational model that tries to simulate the structure ofbiologicalneuralnetworks.Human brainNeuron10105西步交通大学
5 1.1 What is ANN ? PART 1 Human brain 1010 Neuron Artificial neural network is a mathematical model or computational model that tries to simulate the structure of biological neural networks
11.2 Theory of ANNPARTOutputActivation FunctionhardlimpurelinlogsigHiddenInputHiddenOuterLayerLayerLayerLayer10=1+e-net1Vx1net =wixi+.x2.i=0x3x4WoWiW2WnXo=1XXInputx6西步交通大学
6 PART 1 w1 w2 wn w0 x0=1 Output x1 x2 xn . . . Input x j n i net = wixi + =0 net e o − + = 1 1 Activation Function 1.2 Theory of ANN
T1.3 Types of ANNPARTPercentagesofdifferenttypesofANNsBPNusedinnuclearresearchesSOM57.14%PNNGRNNBPNGNNSOMPNNGMDH-ANN7.14%GRNNRBFNGNN1.02%GMDH-ANNANFISRBFN4.08%WNNANFIS2.04%WNNSONN8.16%SONN3.06%1.02%HONN5.1%HONN8.16%1.02%1.02%FCNNFCNN1.02%typenotgiven西步交通大学
7 PART 1 1.3 Types of ANN ➢ BPN ➢ SOM ➢ PNN ➢ GRNN ➢ GNN ➢ GMDH-ANN ➢ RBFN ➢ ANFIS ➢ WNN ➢ SONN ➢ HONN ➢ FCNN ➢ . Percentages of different types of ANNs used in nuclear researches
11.3 Types of ANNPARTBPN (Back PropagationNeural Network)is the mostwidelyusedtypeinnuclearengineeringareaShortcomingsofBPNDifficult to determine the training parameters, such as the面number of hidden layers and number of neuron;II. Relatively low computation efficiency and rate of convergencetime-consuming training;IIl. Probable stop of the computation when reaching the localminimum errorGenetic Algorithm (GA)GNNisan effectivemethod tooptimize BPN8西步交通大学
8 PART 1 1.3 Types of ANN ⚫ Shortcomings of BPN: I. Difficult to determine the training parameters, such as the number of hidden layers and number of neuron; II. Relatively low computation efficiency and rate of convergence, time-consuming training; III. Probable stop of the computation when reaching the local minimum error Genetic Algorithm (GA) is an effective method to optimize BPN BPN (Back Propagation Neural Network) is the most widely used type in nuclear engineering area GNN
11.3 Types of ANNPARTGNN(GeneticNeural Network)usesGeneticAlgorithmtooptimizeBPNStartGeneticAlgorithmCrossoverProblemAnalysisSGA=(C,Ef,Pmi,M,Φ,T,Y,TMutationNeural networkmodelevaluationselectionNConvergencePopulationTXinitializationTrainingFitnesscalculationEndSelectionmutationcrossover9西步交通大学
9 PART 1 1.3 Types of ANN GNN (Genetic Neural Network) uses Genetic Algorithm to optimize BPN SGA C E P M T = ( , , , , , , , f ini e ) Genetic Algorithm Start Problem Analysis Neural network model Population initialization Fitness calculation Selection Crossover Mutation Convergence Training End Y N
西安交通大学XIANJIAOTONG UNIVERSITY02ANNApplications inT/HProblems1896
ANN Applications in T/H Problems