Review on methods to fix number of hidden neurons in neural networks
This paper reviews methods to fix a number of hidden neurons in neural networks for the
past 20 years. And it also proposes a new method to fix the hidden neurons in Elman …
past 20 years. And it also proposes a new method to fix the hidden neurons in Elman …
Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods
Increasing power demand, aging distribution systems and concerns towards greenhouse
gas emissions have resulted in the increased occurrence of distributed generation (DG) …
gas emissions have resulted in the increased occurrence of distributed generation (DG) …
Effects of hidden layers on the efficiency of neural networks
M Uzair, N Jamil - 2020 IEEE 23rd international multitopic …, 2020 - ieeexplore.ieee.org
Hidden layers play a vital role in the performance of Neural network especially in the case of
complex problems where the accuracy and the time complexity are the main constraints. The …
complex problems where the accuracy and the time complexity are the main constraints. The …
Optimizing connection weights in neural networks using the whale optimization algorithm
The learning process of artificial neural networks is considered as one of the most difficult
challenges in machine learning and has attracted many researchers recently. The main …
challenges in machine learning and has attracted many researchers recently. The main …
Time series forecasting using a deep belief network with restricted Boltzmann machines
T Kuremoto, S Kimura, K Kobayashi, M Obayashi - Neurocomputing, 2014 - Elsevier
Multi-layer perceptron (MLP) and other artificial neural networks (ANNs) have been widely
applied to time series forecasting since 1980s. However, for some problems such as …
applied to time series forecasting since 1980s. However, for some problems such as …
[HTML][HTML] Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning
This paper presents a hybrid ensemble classifier combined synthetic minority oversampling
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …
technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and …
[HTML][HTML] A survey on the application of recurrent neural networks to statistical language modeling
In this paper, we present a survey on the application of recurrent neural networks to the task
of statistical language modeling. Although it has been shown that these models obtain good …
of statistical language modeling. Although it has been shown that these models obtain good …
Comprehensive study on applications of artificial neural network in food process modeling
GVS Bhagya Raj, KK Dash - Critical reviews in food science and …, 2022 - Taylor & Francis
Artificial neural network (ANN) is a simplified model of the biological nervous system
consisting of nerve cells or neurons. The application of ANN to food process engineering is …
consisting of nerve cells or neurons. The application of ANN to food process engineering is …
A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment
In this research, monthly wind speed time series of the Kirsehir was investigated using the
stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process …
stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process …
The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network
To decrease the environmental and climatic effects of rising concrete consumption, more
environmentally friendly concretes are required. One approach to achieve this goal is using …
environmentally friendly concretes are required. One approach to achieve this goal is using …