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Full-Text Articles in Engineering
Neural Network And Regression Modeling Of Extrusion Processing Parameters And Properties Of Extrudates Containing Ddgs, Nehru Chevanan, Kasiviswanathan Muthukumarappan, Kurt A. Rosentrater
Neural Network And Regression Modeling Of Extrusion Processing Parameters And Properties Of Extrudates Containing Ddgs, Nehru Chevanan, Kasiviswanathan Muthukumarappan, Kurt A. Rosentrater
Kurt A. Rosentrater
Two sets of experiments using a single-screw extruder were conducted with an ingredient blend containing 40% DDGS (distillers dried grains with solubles), along with soy flour, corn flour, fish meal, vitamin mix, and mineral mix, with the net protein content adjusted to 28%. The variables controlled in the first experiment included seven levels of die size, three levels of moisture content, three levels of temperature gradient in the barrel, and one screw speed. The variables altered in the second experiment included three levels of moisture content, three levels of temperature gradient in the barrel, five levels of screw speed, and …
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping …
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
An artificial neural network can work as an intelligent controller for nonlinear dynamic systems through learning, as it can easily accommodate the nonlinearities and time dependencies. In dealing with complex problems, most common neural networks have some drawbacks of large training time, large number of neurons and hidden layers. These drawbacks can be overcome by a nonlinear controller based on a generalized neuron (GN) which retains the quick response of neural net. Results of studies with a GN-based power system stabilizer on a five-machine power system show that it can provide good damping over a wide operating range and significantly …
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. To overcome these drawbacks, a generalized neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new power system stabilizer (PSS) is proposed. Results show that the proposed GN-based PSS can provide a consistently good dynamic performance of the system over a wide range …