Parity–time symmetric optical neural networks

Symmetric Discrete Universal Neural Networks

Given the class of symmetric discrete weight neural networks with finite state set (0, l}, we prove that there exist iteration modes under these networks which allow to simulate in linear space arbitrary neural networks (non-necessarily symmetric). As a particular result we prove that an arbitrary symmetric neural network can be simulated by a symmetric one iterated sequentially, with some nega...

rodbar 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 ...

Absence of Cycles in Symmetric Neural Networks

For a given recurrent neural network, a discrete-time model may have asymptotic dynamics di erent from the one of a related continuous-time model. In this paper, we consider a discretetime model that discretizes the continuous-time leaky integrator model and study its parallel, sequential, block-sequential and distributed dynamics for symmetric networks. We provide su cient (and in many cases n...

Cycle-symmetric matrices and convergent neural networks

This work investigates a class of neural networks with cycle-symmetric connection strength. We shall show that, by changing the coordinates, the convergence of dynamics by Fiedler and Gedeon [Physica D 111 (1998) 288] is equivalent to the classical results. This presentation also addresses the extension of the convergence theorem to other classes of signal functions with saturations. In particu...

Absence of Cycles in Symmetric Neural Networks