Atualmente, a utilização de sistemas inteligentes híbridos que utilizam combinação técnicas inteligência artificial como, por exemplo, redes neurais e lógica fuzzy, tem-se tornado comuns na elaboração modelos complexos simulação estimar parâmetros desejados. O objetivo deste estudo foi desenvolver inferência adaptativos neurofuzzy (ANFIS) para predizer produção do abacaxizeiro ‘Vitória’ realiza...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

the purpose of this study was to estimate the torque from high‑density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate‑to‑high isometric elbow flexion‑extension. the elbow torque was estimated in two following steps: first, surface electromyography (emg) amplitudes were estimated using principal com...

Drought plays a crucial role in agriculture, especially farming and has significant impact on the environment. The present study focuses forecast of drought using one hybrid artificial neural network namely Adaptive NeuroFuzzy Inference System (ANFIS). For this study, 39 years monthly precipitation value Erode district are used. Firstly, values, Standard Precipitation Index (SPI) values compute...

System identification can be used to construct a model to represent a given system, and it plays an important role in system analysis, control and prediction. From the view of application, conventional nonlinear black-box models are not good since an easy-to-use model is to interpret properties of the nonlinear process, rather than treated as vehicles for adjusting the fit to the data. Therefor...

Pre-programming complex robotic systems to operate in unstructured environments is extremely difficult because of the programmer’s inability to predict future operating conditions in the face of unforeseen environmental conditions, mechanical wear of parts, etc. The solution to this problem is for the robot controller to learn on-line about its own capabilities and limitations when interacting ...

Disadvantages of traditional reinforcement learning techniques are complicated structures and that training algorithms are often reliant on the derivative information of the problem domain and also require a priori information of the network architecture. Such handicaps are overcome in this paper with the use of ‘messy genetic algorithms’, whose main characteristic is a variable length chromoso...

Empirical modelling in high dimensional spaces is usually preceded by a feature selection stage. Irrelevant or noisy features unnecessarily increase the complexity of the problem and can degrade modelling performance. Here, multiobjective genetic algorithms are proposed as effective means of evolving a diverse population of alternative feature sets with various accuracy/complexity trade-offs. T...