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 ...
Scientific Research Publishing

Buhmann, M.D. (2003) Radial Basis Functions. Cambridge University Press.

ABSTRACT: We solve numerically an eigenvalue elliptic partial differential equation (PDE) ranging from two to six dimensions using the generalized multiquadric (GMQ) radial basis functions (RBFs). Two ...
Hosted on MSN

What Is An Activation Function In A Neural Network? (Types Explained Simply)

Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! #NeuralNetworks ...
Bloomberg L.P.

Fed Urged to Explore Hedge Fund Bailout Tool for Basis Trade

The Federal Reserve should consider setting up an emergency program that would close out highly leveraged hedge-fund trades in the event of a crisis in the $29 trillion US Treasuries market, according ...
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 ...