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The artificial neural network was trained using a hybrid learning method based on Simulated Annealing and Levenberg Marquardt method.
Training of each neuro-fuzzy sub-component in ensemble network is carried out using a hybrid learning scheme.
Using a hybrid learning procedure, ANFIS can construct an input output mapping based on both human knowledge (in the form of fuzzy if then rules) and stipulated input output data pairs.
In ANFIS, the parameters associated the membership functions of input and output are trained using a hybrid learning algorithm that combines the least-square estimator and the gradient descent method (Jang 1993).
To achieve this phase of ANFIS system learning, a gradient descent algorithm with a least squares estimation using a hybrid learning rule is proposed as shown in Table 3.
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The ANFIS procedure may use a hybrid-learning algorithm to identify the membership function parameters of single-output, the Sugeno type fuzzy inference system.
In HLPSO-GD, the swarm uses a hybrid learning strategy whereby all other particles' previous best information is adopted to update a particle׳s position.
This research was intended to develop an analytical model for enhancing the service quality of e-learning using a hybrid approach from the perspective of customers.
In this paper, we propose a multi-class learning method of multiconlitron by using a hybrid binary tree architecture.
An improved 191Os/191mIr generator using a hybrid anion exchanger.
Using a hybrid drive makes sense for a few reasons.
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