Differential Learning Algorithm for Artificial Neural Networks
نویسندگان
چکیده
منابع مشابه
Tuning Differential Evolution For Artificial Neural Networks
The efficacy of an optimization method often depends on the choosing of a number of behavioural parameters. Research within this area has been focused on devising schemes for adapting the behavioural parameters during optimization, so as to alleviate the need for a practitioner to select the parameters manually. But these schemes usually introduce new behavioural parameters that must be tuned. ...
متن کاملComputational Learning Theory for Artificial Neural Networks
There are many types of activity which are commonly known as ‘learning’. Here, we shall discuss a mathematical model of one such process, known as the the ‘probably approximately correct’ (or PAC) model. We shall illustrate how key problems of learning in artificial neural networks can be studied within this framework, presenting theoretical analyses of two important issues: the size of trainin...
متن کاملTailoring Artificial Neural Networks for Optimal Learning
As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine to finance, and language processing. A key feature of the ESN paradigm is its reservoir —a directed and weighted network— that represents the connections between neurons and projects the input signals into a high dimensional spac...
متن کاملEfficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks
In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for...
متن کاملArtificial Neural Networks, Symmetries and Differential Evolution
Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local se...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2010
ISSN: 0975-8887
DOI: 10.5120/381-571