Neural Networks are computational models inspired by the structure and function of biological neural networks in the brain.
Types of Neural Networks
- Feedforward Neural Networks — the simplest type, where information moves in one direction
- Recurrent Neural Networks (RNNs) — connections between nodes form a directed graph along a temporal sequence
- Convolutional Neural Networks (CNNs) — primarily used for image and video processing
- Transformer Networks — attention-based models for sequence-to-sequence learning
Applications
- Computer Vision
- Natural Language Processing
- Speech Recognition
- Reinforcement Learning — neural networks can approximate value functions and policies
- NN-ADRC — Neural Network in Active Disturbance Rejection Control for aerospace
- Pattern Recognition
Related Concepts
- Extended Kalman Filter — State estimation using neural network approximations
- Optuna Hyperparameter Tuning — Tuning neural network architectures
- Fault Detection and Isolation — Using NN for fault detection
- Control Allocation — NN-based control allocation
Key Concepts
- Backpropagation
- Activation Functions
- Deep Learning
- Machine Learning