Description: Fuzzy Neural Networks with Genetic Algorithms represent a hybrid approach that combines the learning capability of neural networks with the flexibility of fuzzy systems and the optimization of genetic algorithms. This multimodal model is based on the idea that neural networks can benefit from fuzzy logic to handle uncertainty and imprecision in data, while genetic algorithms can optimize the parameters and structure of the network. Fuzzy neural networks allow for the representation of information in the form of fuzzy sets, facilitating decision-making in complex environments. On the other hand, genetic algorithms, inspired by the natural evolution process, use mechanisms such as selection, crossover, and mutation to find optimal solutions to complex problems. The combination of these two approaches enables the creation of more robust and adaptive models, capable of learning and generalizing from noisy or incomplete data. This approach has become relevant in various fields, such as artificial intelligence, signal processing, and system optimization, where the ability to handle uncertainty and find optimal solutions is crucial for the success of applications.
