Research Article
An Extreme Learning Machine Based on Artificial Immune System
Algorithm 3
Artificial immune system extreme learning machine.
Step 1 Initialization | Randomly generate the initial antibody population | where . | Then calculate the fitness of by Eq.(12), | and get . | Step 2 Clone Selection | while the stop criterion is not met with do | Step 2.1 Clone Phase | For each antibody clone N-1 antibody, the clone pool named where | = | Step 2.2 Mutation Phase | For each clone antibody , where and | | | | where is computed by Eq.(10). | Then compute the fitness of by Eq.(12). | And get . | Step 2.3 Substitution Phase | For each antibody , compare and | For | | | end while | The final antibody population is and with the smallest fitness is the best antibody . Then | is used to opitimize the weight. | Step 3 ELM | Calculate the hidden-layer output matrix with the set of input weights and hidden biases represented by the . | Compute the output weights matrix . |
|