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back propagation algorithm

در نشریات گروه ریاضی
تکرار جستجوی کلیدواژه back propagation algorithm در نشریات گروه علوم پایه
تکرار جستجوی کلیدواژه back propagation algorithm در مقالات مجلات علمی
  • Fatemeh Ahmadkhanpour, Hossein Kheiri *, Nima Azarmir, Farzin Modarres Khiyabani
    This research introduces a novel approach using artificial neural networks (ANNs) to tackle ordinary differential equations (ODEs) through an innovative technique called enhanced back-propagation (EBP). The ANNs adopted in this study, particularly multilayer perceptron neural networks (MLPNNs), are equipped with tunable parameters such as weights and biases. The utilization of MLPNNs with universal approximation capabilities proves to be advantageous for ODE problem solving. By leveraging the enhanced back-propagation algorithm, the network is fine-tuned to minimize errors during unsupervised learning sessions. To showcase the effectiveness of this method, a diverse set of initial value problems for ODEs are solved and the results are compared against analytical solutions and conventional techniques, demonstrating the superior performance of the proposed approach.
    Keywords: Artificial Neural Networks, Ordinary Differential Equations, Back-Propagation Algorithm
  • Elham Bideh, Mohammadreza Fadavi Amiri, Javad Vahidi *, Majid Iranmanesh
    Today, computer network fault diagnosis is one of the key challenges experts are facing in the field of computer networks.  Therefore, achieving an automatic diagnosis system which is based on artificial intelligence methods and is able to diagnose faults with maximum accuracy and speed is of high importance. One of the methods which is studied and utilized up to now is artificial neural networks with a back propagation algorithm while using neural networks with a back propagation algorithm has two main challenges in front. The first challenge is related to the backpropagation learning type as it is a supervised learning requiring inductive knowledge driven from previous conditions. The second challenge is the long time required for training such a neural network. In this work, combining neural networks with a backpropagation algorithm and fuzzy logic is applied as a method for confronting these challenges. The result of this study shows that fuzzy clustering is able to provide the inductive knowledge required for backpropagation learning by determining the membership degree of training samples to different clusters of network faults. Also, according to the simulations taken place, implementing a fuzzy controller in determining the learning rate in each backpropagation iteration has resulted in successful outcomes. Thus, the learning speed of this algorithm has been increased in comparison to the constant learning rate mode resulted in reducing the training time duration of this neural networks.
    Keywords: Computer Networks Fault Diagnosis, Artificial Neural Networks, Back Propagation Algorithm, Fuzzy Clustering, Fuzzy Controller
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