인공 신경망을 교육 합니다. XOR 문제입니다. 변론 단위, 바이어스와 로그 시그모이드 신경. 입력된 계층: 2, 숨겨진 레이어: 2, 출력 계층: 1 신경. 원하는 및 실제 출력 사이의 평균 제곱 오류를 반환합니다. 기준 논문: 디 Karaboga, B. Basturk Akay, C. Ozturk, 훈련 피드 포워드 신경 네트워크, LNCS에 대 한 인공 꿀벌 식민지 (ABC) 최적화 알고리즘: 인공 지능, 2007-4617, 318-329, 2007에 대 한 결정을 모델링 합니다. D. Karaboga, C. Ozturk Trainin 신경망(translate from): Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */- Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */




File list:
ABCNNTrain
.........\calculateFitness.m
.........\GreedySelection.m
.........\nntrainxor221b.m
.........\runABC.m
.........\ScoutSelection.m