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Activation functions in neural networks: Types and examples

In this video, we will see What is Activation Function in Neural network, types of Activation function in Neural Network, why to use an Activation Function and which Activation function to use. The ...
Frontiers

Editorial: Advancements in neural learning control for enhanced multi-robot coordination

Multi-robot systems are increasingly deployed in complex, dynamic environments such as environmental monitoring, industrial automation, and search-and-rescue missions. The coordination of such systems ...
Ars Technica

Neural network finds an enzyme that can break down polyurethane

You’ll often hear plastic pollution referred to as a problem. But the reality is that it’s multiple problems. Depending on the properties we need, we form plastics out of different polymers, each of ...
IEEE

Using Radial Basis Function Neural Network With Weight Memory Mechanism: Nonlinear System Identification

Abstract: The radial basis function neural network (RBFNN) is a learning model with better generalization ability, which attracts much attention in nonlinear system identification. Compared with the ...
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Cost Functions In Neural Networks Explained – Which One Should You Use And Why?

Confused about cost functions in neural networks? In this video, we break down what cost functions are, why they matter, and which types are best for different applications—from classification to ...
Scientific Research Publishing

Rawat, A., Yadav, R.N. and Shrivastava, S.C. (2013) Neural Modeling of Antenna Array Using Radial Basis Neural Network for Directivity Prediction. International Journal of ...

ABSTRACT: With the advent of the 5G and future 6G, base stations will be used as station controllers. The antenna systems are networked and equipped with a processor to optimize the detection of ...
Science Daily

A robust and adaptive controller for ballbots

Ballbots are versatile robotic systems with the ability to move around in all directions. This makes it tricky to control their movement. In a recent study, a team has proposed a novel proportional ...
IEEE

Neural Network Models for Predicting Magnetization Surface Switched Reluctance Motor: Classical, Radial Basis Function, and Physics-Informed Techniques

Abstract: Neural networks have increasingly been utilized in electric drive systems to enhance modeling, control, and optimization. These data-driven techniques enable accurate predictions of complex ...
GitHub

Add Radial Basis Function Neural Network (RBFNN)

Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regression and classification ...