Rocio Carrasco

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Three Western studies have shown that male-to-female (MF) homosexual transsexuals tend to be born later than their siblings and to come from sibships with more brothers than sisters. The objective of this study was to determine whether these variables would be replicated in 530 MF and female-to-male (FM) Spanish transsexuals according to sexual orientation.(More)
This paper presents a hybrid intelligent control based on nonlinear PI controller and a recurrent high order neural network (RHONN) identifier. This control scheme is applied to a wastewater treatment prototype. The hybrid intelligent control and neuronal identification performance is illustrated via simulations.
This paper presents neuronal network identification of a wastewater treatment prototype. This identification is based on a discrete-time high order neuronal network (RHONN). The neuronal network is trained with an extended Kalman filter (EFK) algorithm. The neuronal identification performance is illustrated via simulations.
In this paper, a discrete-time neural control scheme to regulate carbon monoxide (CO) and nitrogen oxides (NO<sub>x</sub>) emissions for a fluidized bed sludge incinerator is proposed. Carbon monoxide emissions are reduce by oxygen regulation in the incinerator; nevertheless nitrogen oxides emissions are difficult to control because the sludge composition(More)
In this work, a neural control scheme to regulate carbon monoxide (CO) and nitrogen oxides (NO<sub>x</sub>) emissions for a solid waste incinerator is proposed. Carbon monoxide emissions are avoided by oxygen regulation in the incinerator; nevertheless nitrogen oxides emissions are difficult to control because the sludge composition varies continuously. The(More)
In this paper, the authors propose a discrete-time neural control scheme to regulate nitrogen oxides (NOx) emissions for a fluidized bed sludge combustor. This scheme ensures carbon monoxide (CO) regulation without decreasing combustion efficiency. In order to obtain the sludge combustion model, it is proposed to use a recurrent high order neural network(More)
This paper presents a neural network application to identify a kinetic model for the char reduction zone of a solid fuel gasification process, including input signals. The considered model consists of six differential equations which represent the production of six components (carbon, hydrogen, carbon monoxide, water, carbon dioxide and methane) and are(More)
This paper presents a neural network application to identify a kinetic model for the char reduction zone of a solid fuel gasification process. The considered model consists of six differential equations which represent the production of six components (carbon, hydrogen, carbon monoxide, water, carbon dioxide and methane) and are obtained from reaction rate(More)
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