Intelligence in the brain: A theory of how it works and how to build it
PJ Werbos - Neural Networks, 2009 - Elsevier
This paper presents a theory of how general-purpose learning-based intelligence is
achieved in the mammal brain, and how we can replicate it. It reviews four generations of …
achieved in the mammal brain, and how we can replicate it. It reviews four generations of …
A comprehensive review on RSM-coupled optimization techniques and its applications
A Susaimanickam, P Manickam, AA Joseph - Archives of Computational …, 2023 - Springer
This review article provides a comprehensive analysis of the optimization techniques used
in a wide range of engineering applications. The comparison of various approaches such as …
in a wide range of engineering applications. The comparison of various approaches such as …
Neural network for graphs: A contextual constructive approach
A Micheli - IEEE Transactions on Neural Networks, 2009 - ieeexplore.ieee.org
This paper presents a new approach for learning in structured domains (SDs) using a
constructive neural network for graphs (NN4G). The new model allows the extension of the …
constructive neural network for graphs (NN4G). The new model allows the extension of the …
Integrating Elman recurrent neural network with particle swarm optimization algorithms for an improved hybrid training of multidisciplinary datasets
There are several types of neural networks (NNs) that are widely used for data classification
tasks. The supervised learning NN is an advanced network with a training algorithm for …
tasks. The supervised learning NN is an advanced network with a training algorithm for …
Training recurrent neural networks with the Levenberg–Marquardt algorithm for optimal control of a grid-connected converter
This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-
Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected …
Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected …
Nonlinear model identification and adaptive model predictive control using neural networks
This paper presents two new adaptive model predictive control algorithms, both consisting of
an on-line process identification part and a predictive control part. Both parts are executed at …
an on-line process identification part and a predictive control part. Both parts are executed at …
Analysis of direct selection in head-mounted display environments
The design of 3D user interfaces (3DUIs) for immersive head-mounted display (HMD)
environments is an inherently difficult task. The fact that usually haptic feedback is absent …
environments is an inherently difficult task. The fact that usually haptic feedback is absent …
Goal representation heuristic dynamic programming on maze navigation
Goal representation heuristic dynamic programming (GrHDP) is proposed in this paper to
demonstrate online learning in the Markov decision process. In addition to the (external) …
demonstrate online learning in the Markov decision process. In addition to the (external) …
Novel maximum-margin training algorithms for supervised neural networks
This paper proposes three novel training methods, two of them based on the
backpropagation approach and a third one based on information theory for multilayer …
backpropagation approach and a third one based on information theory for multilayer …
Sparse simultaneous recurrent deep learning for robust facial expression recognition
Facial expression recognition is a challenging task that involves detection and interpretation
of complex and subtle changes in facial muscles. Recent advances in feed-forward deep …
of complex and subtle changes in facial muscles. Recent advances in feed-forward deep …