An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

By G. Huang, Paramasivan Saratchandran, Narasimhan Sundararajan. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004

400 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/bacb78a08252349bd6c0ccfbfd5c29685f4d3f3a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. G. Huang
2. Yuan Lan
3. Lei Chen
4. Dianhui Wang
5. Narasimhan Sundararajan
6. Paramasivan Saratchandran
7. Nan-Ying Liang
8. Chee Kheong Siew

In Computer Vision and Pattern Recognition

1. G. Huang
2. Yuan Lan
3. Dianhui Wang
4. Narasimhan Sundararajan
5. Paramasivan Saratchandran
6. Lei Chen
7. Nan-Ying Liang
8. Lihua Xie

In Control and Systems Engineering

1. G. Huang
2. Lihua Xie
3. Narasimhan Sundararajan
4. Chee Kheong Siew
5. Paramasivan Saratchandran
6. Dianhui Wang
7. Huijun Gao
8. Tongwen Chen

Citations in the corpus

• FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (2008, neutral): “GAP-RBF neural network proposed in [20].”
• Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes (2006, background): “Compared to other popular algorithms such as support vector regression (SVR) [17], [18], stochastic gradient descent backpropagation (BP) [19], and incremental RBF networks (RAN [20], RANEKF [21], MRAN [22], [23], GAP-RBF [24], and GGAP-RBF [25]), the proposed incremental SLFN is fully automatic in ”
• An online sequential extreme learning machine approach to WiFi based indoor positioning (2014, methodology): “Differently from other online sequential learning algorithms such as SGBP [7] and GAP-RBF [8] which require specific types of hidden nodes, OS-ELM is able to adapt to various types of hidden nodes.”
• Advantages of Radial Basis Function Networks for Dynamic System Design (2011, methodology): “In this example, the proposed ErrCor algorithm is compared with another hierarchical growing/pruning strategy (GGAP algorithm) for network construction presented in a well-cited paper [40].”
• Extreme learning machines: a survey (2011, background): “resource allocation network and its variants [57, 58, 60, 63, 64] work only for RBF networks), I-ELM can work well with a wide type of activation functions no matter whether they are sigmoidal or nonsigmoidal, continuous or noncontinuous, and differentiable or non-differentiable.”
• A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks (2006, methodology): “Sequential learning algorithms like SGBP [11], RAN [14], RANEKF [15], MRAN [16], [17], GAP-RBF [18], GGAP-RBF [19], and OS-ELM can be used only in the one-by-one mode.”