Systems Analysis Modelling Simulation
Time-Scaling in Recurrent Neural Learning
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Proceedings of the Second European Workshop on Genetic Programming
Accelerated Backpropagation Learning: Extended Dynamic Parallel Tangent Optimization Algorithm
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Discovering efficient learning rules for feedforward neural networks using genetic programming
Recent advances in intelligent paradigms and applications
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Back propagation with randomized cost function for training neural networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Engineering Applications of Artificial Intelligence
Biologically-inspired visual-motor coordination model in a navigation problem
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning