AIMC Topic: Neural Networks, Computer

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Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.

Bioresource technology
A novel approach to overcome the acidification problem has been attempted in the present study by codigesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codigestion of the weed and PW was tested in...

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

Environmental monitoring and assessment
This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passin...

Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

Computational intelligence and neuroscience
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parame...

Prediction of Cascading Failures in Spatial Networks.

PloS one
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction...

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

Nucleic acids research
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of fun...

Non-Hermitian localization in biological networks.

Physical review. E
We explore the spectra and localization properties of the N-site banded one-dimensional non-Hermitian random matrices that arise naturally in sparse neural networks. Approximately equal numbers of random excitatory and inhibitory connections lead to ...

Learning With Jensen-Tsallis Kernels.

IEEE transactions on neural networks and learning systems
Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probab...

MIMO transmit scheme based on morphological perceptron with competitive learning.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The...

Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the multistability for a general class of recurrent neural networks with time-varying delays. Without assuming the linearity or monotonicity of the activation functions, several new sufficient conditions are obtained to ensure th...

Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units.

Neural networks : the official journal of the International Neural Network Society
The restricted Boltzmann machine (RBM) is an essential constituent of deep learning, but it is hard to train by using maximum likelihood (ML) learning, which minimizes the Kullback-Leibler (KL) divergence. Instead, contrastive divergence (CD) learnin...