AIMC Topic: Neural Networks, Computer

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The prediction in computer color matching of dentistry based on GA+BP neural network.

Computational and mathematical methods in medicine
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back p...

A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.

Environmental monitoring and assessment
Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The pres...

Modelling the longevity of dental restorations by means of a CBR system.

BioMed research international
The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may pre...

A time-series model of phase amplitude cross frequency coupling and comparison of spectral characteristics with neural data.

BioMed research international
Stochastic processes that exhibit cross-frequency coupling (CFC) are introduced. The ability of these processes to model observed CFC in neural recordings is investigated by comparison with published spectra. One of the proposed models, based on mult...

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

IEEE transactions on neural networks and learning systems
Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, ...

Finite-Horizon Near-Optimal Output Feedback Neural Network Control of Quantized Nonlinear Discrete-Time Systems With Input Constraint.

IEEE transactions on neural networks and learning systems
The output feedback-based near-optimal regulation of uncertain and quantized nonlinear discrete-time systems in affine form with control constraint over finite horizon is addressed in this paper. First, the effect of input constraint is handled using...

A new delay-independent condition for global robust stability of neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bo...

Perceptrons with Hebbian learning based on wave ensembles in spatially patterned potentials.

Physical review letters
A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrödinger waves. Spatially patterned potentials process information by coupling different points ...

Large-scale transportation network congestion evolution prediction using deep learning theory.

PloS one
Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely...

Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle.

IEEE transactions on neural networks and learning systems
High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear pr...