@article{DBLP:journals/eaai/VieiraDM04,

title = {Artificial neural networks and neuro-fuzzy systems for modelling and 

 controlling real systems: a comparative study},

author = {José Vieira and Fernando Morgado Dias and Alexandre Mota},

year  = {2004},

date = {2004-01-01},

journal = {Eng. Appl. Artif. Intell.},

volume = {17},

number = {3},

pages = {265--273},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/eaai/DiasAM04,

title = {Artificial neural networks: a review of commercial hardware},

author = {Fernando Morgado Dias and Ana Antunes and Alexandre Manuel Mota},

year  = {2004},

date = {2004-01-01},

journal = {Eng. Appl. Artif. Intell.},

volume = {17},

number = {8},

pages = {945--952},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9210b,

title = {Fault Tolerance of Artificial Neural Networks: an Open Discussion for a Global Model},

author = {Dias F. Morgado and Ana Antunes},

journal = {International Journal of Circuits, Systems and Signal Processing [Impact Factor SJR2012: 0.201]},

volume = {4},

pages = {9-16},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8437b,

title = {A survey of artificial neural network training tools},

author = {Dario Baptista and Dias F. Morgado},

url = {http://dx.doi.org/10.1007/s00521-013-1408-9},

doi = {10.1007/s00521-013-1408-9},

issn = {0941-0643},

journal = {Neural Computing and Applications [Impact Factor SJR2012: 1.168]},

volume = {23},

number = {3-4},

pages = {609-615},

publisher = {Springer London},

abstract = {<p>Artificial neural networks (ANN) are currently an additional tool which the engineer can use for a variety of purposes. Classification and regression are the most common tasks; however, control, modeling, prediction and forecasting are common tasks as well. For over three decades, the field of ANN has been the center of intense research. As a result, one of the outcomes has been the development of a large set of software tools used to train these kinds of networks, making the selection of an adequate tool difficult for a new user. This paper aims to help the ANN user choose the most appropriate tool for its application by providing a large survey of the solutions available, as well as listing and explaining their characteristics and terms of use. The paper limits itself to focusing on the tools which were developed for ANN and the relevant characteristics of these tools, such as the operating systems, hardware requirements, license types, architectures and algorithms available.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8437,

title = {A survey of artificial neural network training tools},

author = {Dario Baptista and Dias F Morgado},

url = {http://dx.doi.org/10.1007/s00521-013-1408-9},

doi = {10.1007/s00521-013-1408-9},

issn = {0941-0643},

journal = {Neural Computing and Applications [Impact Factor SJR2012: 1.168]},

volume = {23},

number = {3-4},

pages = {609-615},

publisher = {Springer London},

abstract = {<p>Artificial neural networks (ANN) are currently an additional tool which the engineer can use for a variety of purposes. Classification and regression are the most common tasks; however, control, modeling, prediction and forecasting are common tasks as well. For over three decades, the field of ANN has been the center of intense research. As a result, one of the outcomes has been the development of a large set of software tools used to train these kinds of networks, making the selection of an adequate tool difficult for a new user. This paper aims to help the ANN user choose the most appropriate tool for its application by providing a large survey of the solutions available, as well as listing and explaining their characteristics and terms of use. The paper limits itself to focusing on the tools which were developed for ANN and the relevant characteristics of these tools, such as the operating systems, hardware requirements, license types, architectures and algorithms available.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9210,

title = {Fault Tolerance of Artificial Neural Networks: an Open Discussion for a Global Model},

author = {Dias F Morgado and Ana Antunes},

journal = {International Journal of Circuits, Systems and Signal Processing [Impact Factor SJR2012: 0.201]},

volume = {4},

pages = {9-16},

keywords = {},

pubstate = {published},

tppubtype = {article}

}