Learn about Radial Basis Functions Neural Networks (RBFNNs), a type of neural network that groups data based on distance from a center point. Perfect for CNN certification exam preparation.
Table of Contents
Question
In which type of neural network, the data is grouped based on its distance from a center point?
A. Convolution Neural Network
B. Recurrent Neural Network
C. Modular Neural Network
D. Radial Basis Functions Neural Network
Answer
D. Radial Basis Functions Neural Network
Explanation
Radial Basis Functions Neural Networks (RBFNNs) are designed to group data based on its distance from a central point, making them ideal for pattern recognition and interpolation tasks. This architecture consists of three layers:
- Input Layer: Transmits the input features to the hidden layer.
- Hidden Layer: Utilizes Radial Basis Functions (e.g., Gaussian functions) as activation functions. Each neuron in this layer computes the distance between an input vector and a fixed center point, applying a non-linear transformation based on the distance.
- Output Layer: Produces the final result, combining the transformed inputs using weights optimized during training.
This distance-based grouping allows RBFNNs to model complex, non-linear relationships effectively, making them particularly useful in classification and regression problems where spatial or distance relationships are critical. Unlike Convolutional Neural Networks (CNNs), which excel at image processing, or Recurrent Neural Networks (RNNs), optimized for sequential data, RBFNNs are specialized for tasks involving geometric proximity analysis.
Convolutional Neural Network CNN certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Convolutional Neural Network CNN exam and earn Convolutional Neural Network CNN certification.