Parameter conjugate gradient with secant equation based elman neural network and its convergence analysis

Keyphrases

• Adaptive Step Size 33%
• Classification Function 33%
• Classification Problem 33%
• Classification via Regression 33%
• Conjugate Gradient 100%
• Conjugate Gradient Method 66%
• Convergence Analysis 100%
• Descent Direction 33%
• Elman Neural Network 100%
• Error Function 33%
• Fast Convergence 33%
• Feedback Mechanism 33%
• Function Approximation 33%
• Gradient Method 66%
• Learning Accuracy 33%
• Numerical Experiments 33%
• Recursive Networks 33%
• Sawtooth 33%
• Secant 100%
• Strong Convergence 33%
• Theoretical Convergence 33%
• Weak Convergence 33%
• Wolfe Conditions 33%

Computer Science

• Conjugate Gradient Method 66%
• Conjugate Gradients 100%
• Convergence Speed 33%
• Descent Direction 33%
• Error Function 33%
• Experimental Result 33%
• Fast Convergence 33%
• Feedback Mechanism 33%
• Function Approximation 33%
• Gradient Descent Method 33%
• Mathematical Convergence 100%
• Neural Network 100%
• Objective Function 33%
• Weak Convergence 33%

Mathematics

• Approximation Function 33%
• Conjugate Gradient Method 66%
• Convergence Analysis 100%
• Descent Direction 33%
• Error Function 33%
• Neural Network 100%
• Numerical Experiment 33%
• Objective Function 33%
• Parametric 33%
• Secant 100%
• Step Size 33%
• Strong Convergence 33%
• Weak Convergence 33%