AIMC Topic: Neural Networks, Computer

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Deep Convolutional Neural Networks for large-scale speech tasks.

Neural networks : the official journal of the International Neural Network Society
Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesiz...

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

Neural networks : the official journal of the International Neural Network Society
The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian...

Modelling personal exposure to particulate air pollution: an assessment of time-integrated activity modelling, Monte Carlo simulation & artificial neural network approaches.

International journal of hygiene and environmental health
An experimental assessment of personal exposure to PM10 in 59 office workers was carried out in Dublin, Ireland. 255 samples of 24-h personal exposure were collected in real time over a 28 month period. A series of modelling techniques were subsequen...

Global exponential synchronization of multiple memristive neural networks with time delay via nonlinear coupling.

IEEE transactions on neural networks and learning systems
This paper presents theoretical results on the global exponential synchronization of multiple memristive neural networks with time delays. A novel coupling scheme is introduced, in a general topological structure described by a directed or undirected...

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction.

IEEE transactions on neural networks and learning systems
A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. ...

Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method.

IEEE transactions on neural networks and learning systems
This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estima...

A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.

IEEE transactions on neural networks and learning systems
Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determi...

New synchronization criteria for memristor-based networks: adaptive control and feedback control schemes.

Neural networks : the official journal of the International Neural Network Society
In this paper, we investigate synchronization for memristor-based neural networks with time-varying delay via an adaptive and feedback controller. Under the framework of Filippov's solution and differential inclusion theory, and by using the adaptive...

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

IEEE transactions on neural networks and learning systems
In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed...

Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial Locomotion.

IEEE transactions on neural networks and learning systems
This paper presents an algorithm, self-organizing map-state trajectory generator (SOM-STG), to plan and control legged robot locomotion. The SOM-STG is based on an SOM with a time-varying structure characterized by constructing autonomously close-sta...