منابع مشابه
Hierarchical Evolution of Neural Networks
Inmost applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more efcient genetic search. This paper demonstrates that while SANE can solve easy tasks very quickly, it often stalls in larger problems. A hierarchical approach to neuro-evol...
متن کاملHierarchical Multiscale Recurrent Neural Networks
Learning both hierarchical and temporal representation has been among the longstanding challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchic...
متن کاملrodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Hierarchical self-programming in recurrent neural networks
We study self-programming in recurrent neural networks where both neurons (the ‘processors’) and synaptic interactions (‘the programme’) evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of L groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programm...
متن کاملHierarchical Bayesian Neural Networks for Personalized Classification
Building robust classifiers trained on data susceptible to group or subject-specific variations is a challenging yet common problem in pattern recognition. Hierarchical models allow sharing of statistical strength across groups while preserving group-specific idiosyncrasies, and are commonly used for modeling such grouped data [3]. We develop flexible hierarchical Bayesian models that parameter...
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ژورنال
عنوان ژورنال: Computers & Chemical Engineering
سال: 1992
ISSN: 0098-1354
DOI: 10.1016/0098-1354(92)80053-c