Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

Cilimkovic, M. (2015) Neural Networks and Back Propagation Algorithm. Institute of Technology Blanchardstown, 15, 3-7.

Humans are better than current AI models at interpreting social interactions and understanding social dynamics in moving scenes. Researchers believe this is because AI neural networks were ...

Both, human brain and modern artificial neural networks are extremely powerful. At the lowest level, the neurons work together as rather simple computing units.

Accordingly, this paper proposes a neural network-based genetic algorithm (NNGA), which significantly reduces the dependency on PIC simulations during optimization and enhances the efficiency of HPVED ...

By applying the genetic algorithm to MUSIC and a process of genetic mutation, we can reduce the latency of the linear antenna by about 70%. The running time of the algorithm leads us to explore neural ...

Design of the BP neural network based on genetic algorithm optimization In the damage identification process, if the BP neural network is used alone, there is a high possibility of unfriendly ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

Moreover, inadequate measured output current-voltage (I-V) data make it difficult for conventional meta-heuristic algorithms to obtain a high-quality optimum for solar cell modeling without a reliable ...

Back-Propagation (BP) neural networks is one of most mature neural networks models. It has better self-learning, self-adapted, robustness and generalization ability and has been widely applied to ...