MLP+H: a Hybrid Neural Architecture Formed by the Interaction of Hopfield and Multi-Layer Perceptron Neural Networks

2003 ◽  
pp. 166-173 ◽  
Author(s):  
Clayton Silva Oliveira e Em’ilio De Hernandez
Keyword(s):  
Neural Networks ◽  
Multi Layer Perceptron ◽  
Neural Architecture

scholarly journals MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION

2017 ◽  
Vol 18 (2) ◽  
pp. 224-240 ◽  
Author(s):  
Chi GUOTAI ◽  
Mohammad Zoynul ABEDIN ◽  
Fahmida E–MOULA
Keyword(s):  
Neural Networks ◽  
Risk Profile ◽  
Multi Layer Perceptron ◽  
Neural Models ◽  
Prediction Modeling ◽  
Previous Evidence ◽  
Neural Architecture ◽  
Bank Managers ◽  
Different Types ◽  
Optimal Approach

This study proposes an investigation and optimization of Multi-Layer Perceptron (MLP) based artificial neural networks (ANN) credit prediction model, combine with the effect of different ratios of training to testing instances over five real-world credit databases. As an outcome from the alteration procedure, three different types of hidden units [K = 9 (ANN–1), K = 10 (ANN–2), K = 23 (ANN–3)] are chosen through the pilot experiments and execute, therefore, 45 (5×3×3) unique neural models. Experimental results indicate that “the neural architecture with ten hidden units” is proposed as an optimal approach to classifying the credit information. With these contributions, therefore, we complement previous evidence and modernize the methods of credit prediction modeling. This study, however, has realistic implications for bank managers and other stakeholders to delineate the risk profile of the credit customers.

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

scholarly journals Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

2012 ◽  
Vol 16 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
A. El-Shafie ◽  
A. Noureldin ◽  
M. Taha ◽  
A. Hussain ◽  
M. Mukhlisin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Rainfall Time Series ◽  
Hydrological Process ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Unseen Data

Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

scholarly journals The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments

2020 ◽  
Vol 245 ◽  
pp. 06003
Author(s):  
Venkitesh Ayyar ◽  
Wahid Bhimji ◽  
Lisa Gerhardt ◽  
Sally Robertson ◽  
Zahra Ronaghi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Particle Physics ◽  
Simulated Data ◽  
Image Data ◽  
Use Case ◽  
Neural Architecture ◽  
Ice Cube ◽  
2D And 3D ◽  
Physics Experiments

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.

A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks

2021 ◽  
pp. 2150035
Author(s):  
Yu Xue ◽  
Pengcheng Jiang ◽  
Ferrante Neri ◽  
Jiayu Liang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Structure ◽  
Ad Hoc ◽  
Network Design Problem ◽  
Multi Objective ◽  
Neural Architecture ◽  
Mutation Operators ◽  
Small Structure ◽  
High Flexibility

With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

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