A Neural Network Structure Evolution Algorithm Based on e, m Projections and Model Selection Criterion

2006 ◽  
pp. 732-738
Author(s):  
Yunhui Liu ◽  
Siwei Luo ◽  
Ziang Lv ◽  
Hua Huang
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Network Structure ◽  
Selection Criterion ◽  
Structure Evolution ◽  
Model Selection Criterion ◽  
Neural Network Structure ◽  
Evolution Algorithm

scholarly journals Gap-com: General model selection criterion for sparse undirected gene networks with non-trivial community structure

2021 ◽  
Author(s):  
Markku Kuismin ◽  
Fatemeh Dodangeh ◽  
Mikko J Sillanpää
Keyword(s):  
Model Selection ◽  
Graphical Models ◽  
Network Structure ◽  
Gene Networks ◽  
Gene Network ◽  
Graphical Model ◽  
Regularization Parameter ◽  
Selection Criterion ◽  
Biological Data ◽  
Model Selection Criterion

Abstract We introduce a new model selection criterion for sparse complex gene network modeling where gene co-expression relationships are estimated from data. This is a novel formulation of the gap statistic and it can be used for the optimal choice of a regularization parameter in graphical models. Our criterion favors gene network structure which differs from a trivial gene interaction structure obtained totally at random. We call the criterion the gap-com statistic (gap community statistic). The idea of the gap-com statistic is to examine the difference between the observed and the expected counts of communities (clusters) where the expected counts are evaluated using either data permutations or reference graph (the Erdős-Rényi graph) resampling. The latter represents a trivial gene network structure determined by chance. We put emphasis on complex network inference because the structure of gene networks is usually non-trivial. For example, some of the genes can be clustered together or some genes can be hub genes. We evaluate the performance of the gap-com statistic in graphical model selection and compare its performance to some existing methods using simulated and real biological data example.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1065
Author(s):  
Moshe Bensimon ◽  
Shlomo Greenberg ◽  
Moshe Haiut
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Analog Circuits ◽  
Continuous Time ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Sound Classification ◽  
Classification Framework ◽  
Neural Network Structure ◽  
Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

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