Application of design reuse to artificial neural networks: Case study of the back propagation algorithm [J]. Neural Computing & Applications, 2012, 21(7): 1531–1544.Izeboudjen N; Bouridane A; Farah A;.Application of design reuse to artificial neural networks : Case study of the back propa...
It is highly suitable the BP learning algorithm have been reported to overcome these shortcomings. In this paper, a modified backpropagation algorithm (MBP) based on minimization of the sum of the squares of errors is proposed and implemented on benchmark XOR problem. Implementation results show ...
For MLFF training back-propagation (BP) algorithm and Levenberq-Marquardt (LM) which are based on gradient descent are mostly used [4]. Different techniques have been used in finding an optimal network performance for training ANNs such as Partial Swarm Optimisation (PSO), Differential ...
In this journal, Cheng has proposed a backpropagation (BP) procedure called BPFCC for deep fully connected cascaded (FCC) neural network learning in compar
Advantages of Using the Backpropagation Algorithm in Neural NetworksBefore getting into the details of backpropagation in neural networks, let’s review the importance of this algorithm. Besides improving a neural network, below are a few other reasons why backpropagation is a useful approach: ...
In this paper, we propose a novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm. The proposed algorithm is a modified version of the BP algorithm to improve its convergence behavior in both sides, accelerate the convergence...
A case study of the back propagation (BP) algorithm is proposed. To achieve our goal, the proposed design methodology is based on a modular design of the ANN. The originality of the work is the application of design for reuse (DFR) and the design with reuse (DWR) concepts to ANNs. ...
We built an RNN model to investigate whether the same feedback signals used to control ongoing behaviour could also enable motor adaptation. This work was divided into two phases: (1) training the RNN to perform feedback-based motor control (using a gradient-based algorithm) and (2) using ...
We built an RNN model to investigate whether the same feedback signals used to control ongoing behaviour could also enable motor adaptation. This work was divided into two phases: (1) training the RNN to perform feedback-based motor control (using a gradient-based algorithm) and (2) using ...
关键词: backpropagation neural nets XOR problem connection weights extended backpropagation learning algorithm heterogeneous processing units local minima neural networks Computer networks Equations 会议名称: Proceedings 1992 IJCNN International Joint Conference on Neural Networks 会议时间: 7-11 Jun 1992 ...