Evaluating the applications of dendritic neuron model with metaheuristic optimization algorithms for crude-oil-production forecasting
The forecasting and prediction of crude oil are necessary in enabling governments to
compile their economic plans. Artificial neural networks (ANN) have been widely used in …
compile their economic plans. Artificial neural networks (ANN) have been widely used in …
Wind power forecasting using optimized dendritic neural model based on seagull optimization algorithm and aquila optimizer
It is necessary to study different aspects of renewable energy generation, including wind
energy. Wind power is one of the most important green and renewable energy resources …
energy. Wind power is one of the most important green and renewable energy resources …
Smart predictive viscosity mixing of CO2–N2 using optimized dendritic neural networks to implicate for carbon capture utilization and storage
AA Ewees, HV Thanh, MAA Al-qaness… - Journal of …, 2024 - Elsevier
Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries,
heat transfer underscores the need for a precise assessment of carbon dioxide (CO 2) and …
heat transfer underscores the need for a precise assessment of carbon dioxide (CO 2) and …
A dendritic neuron model optimized by meta-heuristics with a power-law-distributed population interaction network for financial time-series forecasting
The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in
the long term. At the same time, the dendritic neuron model (DNM) has been shown to be …
the long term. At the same time, the dendritic neuron model (DNM) has been shown to be …
Stochastic adaptive CL-BFGS algorithms for fully complex-valued dendritic neuron model
Y Wang, Z Wang, H Huang - Knowledge-Based Systems, 2023 - Elsevier
This paper proposes two stochastic variance reduced gradient algorithms based on
adaptive complex-valued limited-memory BFGS (ACL-BFGS) algorithm, which are …
adaptive complex-valued limited-memory BFGS (ACL-BFGS) algorithm, which are …
A multi-in and multi-out dendritic neuron model and its optimization
Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have
achieved unprecedented success in various fields such as computer vision and natural …
achieved unprecedented success in various fields such as computer vision and natural …
Dendritic Neural Network: A Novel Extension of Dendritic Neuron Model
The conventional dendritic neuron model (DNM) is a single-neuron model inspired by
biological dendritic neurons that has been applied successfully in various fields. However …
biological dendritic neurons that has been applied successfully in various fields. However …
Yet another effective dendritic neuron model based on the activity of excitation and inhibition
Neuronal models have remained an important area of research in computer science. The
dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies …
dendritic neuron model (DNM) is a novel neuronal model in recent years. Previous studies …
Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies
A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree
structure and activation function. Effective initialization of its model parameters is crucial for …
structure and activation function. Effective initialization of its model parameters is crucial for …
Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression
Assessing building energy consumption is of paramount significance in sustainability and
energy efficiency (EE) studies. The development of an accurate EE prediction model is …
energy efficiency (EE) studies. The development of an accurate EE prediction model is …