Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

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 ...

High-Frequency Trading Strategy Based on Deep Neural Networks

Training Deep Fourier Neural Networks to Fit Time-Series Data

We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then trained with regularization to improve generalization. A simple dynamic parameter tuning method is employed to adjust both the learning rate and regularization term, such that stability and efficient training a...

Integrated Analysis of Tumor Samples Sheds Light on Tumor Heterogeneity

The heterogeneity of tumor samples is a major challenge in the analysis of high-throughput profiling of tumor biopsies and cell lines. The measured aggregate signals of multigenerational progenies often represent an average of several tumor subclones with varying genomic aberrations and different gene expression levels. The goal of the present study was to integrate copy number analyses from SN...

Fourier neural networks

A new kind of neuron model that has a Fourier-like IN/OUT function is introduced. The model is discussed in a general theoretical framework and some completeness theorems are presented. Current experimental results show that the new model outperforms by a large margin both in representational power and convergence speed the classical mathematical model of neuron based on weighted sum of inputs ...