Fernando Morgado Dias

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Several implementations of Feedforward Neural Networks have been reported in scientific papers. These implementations do not allow the direct use of off-line trained networks. Usually the problem is the lower precision (compared to the software used for training) or modifications in the activation function. In the present work a hardware solution called(More)
The Levenberg-Marquardt algorithm is considered as the most effective one for training Artificial Neural Networks but its computational complexity and the difficulty to compute the trust region have made it very difficult to develop a true iterative version to use in on-line training. The algorithm is frequently used for off-line training in batch versions(More)
This paper presents a technique for improving the fault tolerance capability of Artificial Neural Networks. This characteristic of distributed systems , which is usually pointed out as one of the advantages of this structure hasn't been deeply studied and can be improved in most of the networks. The solution implemented here consists of changing the(More)
This paper presents a neural network delay compensator to reduce the variable sampling to actuation delay effects in networked control systems The compensator action is based on the knowledge of the sampling to actuation delay affecting the system and the control signal. It can be easily added to an existing control system that does not account for the(More)
Natural neural networks greatly benefit from their parallel structure that makes them fault tolerant and fast in processing the inputs. Their artificial counterpart, artificial neural networks, proved difficult to implement in hardware where they could have a similar structure. Although, many circuits have been developed, they usually present problems(More)
Several implementations of Artificial Neural Networks have been reported in scientific papers. Nevertheless, these implementations do not allow the direct use of off-line trained networks because of the much lower precision when compared with the software solutions where they are prepared or modifications in the activation function. In the present work a(More)
This paper presents an hybrid neuro-fuzzy network-priori knowledge model in temperature control of a gas water heater system. The hybrid model consists in a cascade connection of two blocks: an approximate First Principles Model (FPM) and an unknown block. The first principles model is constructed based in the balance equations of the system and in a priori(More)
Artificial Neural Networks have a wide application in terms of research areas but they have never really lived up to the promise they seemed to be in the beginning of the 80s. One of the reasons for this is the lack of hardware for their implementation in a straightforward and simple way. This paper presents a tool to respond to this need: An Automatic(More)