Artificial Neural Networks (ANN) is an intelligent agent capable of being used in the control of nonlinear motions such as motions of a robot arm manipulator. ANN is capable of providing better control ability than traditional methods. The proposed controller has the ability to effectively utilize a large number of sensory information, can process data collectively and is adaptive by default. Using Back Propagation, the ANN is trained to imbibe the parameters of the robot arm manipulators for improved robot stability and suppressed vibration during robot operation. Mathematical models of the ANN are presented. Developed Simulink model is simulatedand simulation result analyzed. Training performance result of 0.024 Root Mean Square Error (RSME) reduction at epoch 2 was achieved. The result show that trained network robot controller is capable of minimizing the system error to almost zero. A hybrid arrangement could be more responsive for better stability as robot arm manipulator controller.
This paper presents a system for controlling an infant skin temperature using ArtificialNeuro-Fuzzy Inference System (ANFIS). Mathematical and Simulink models of the infant and the incubator system were developed. The ANFIS model integrates both the Fuzzy Logic Control (FLC) rules and the Artificial Neural Network (ANN) to produce optimum control for the variation of the skin temperature of the preterm infant inside the incubator. Simulation was carried out using the ANFIS model, open loop incubator and FLC in two instances. A0.9kg and 2.5kg infant masses and postnatal ages of 5 days were used in the first and second instances. Result showedthat in the first instance, the ANFIS model made a 2.2% and 0.82% improvement over the open loop incubator and the FLC respectively. While a 6.5% and 2.3% improvementwere also made by the ANFIS model over the open loop incubator and the FLC in the second instance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.