Using a supervised learning technique, the approach suggested in this research seeks for approximate solution: First, training data is created by solving optimal control problems, and then a neural network is trained with it. Three cars are chosen for the testing on a two-lane road with two-...
This paper presents a neural network approach for solving convex programming problems with equality constraints. After defining the energy function and neural dynamics of the proposed neural network, we show the existence of an equilibrium point at which the neural dynamics becomes asymptotically stable....
By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature ...
Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the ...
Our model is actually a convolutional neural network with two inputs and orthogonal constraints. Our model consists of the following main steps: (1) We adopt word2vec to obtain the raw input vectors and then use CNNs to extract multiple granularity semantic features (2) The multiple ...
A new kind of neural network model for solving optimal control problems with lower and upper bound constraints on both state and control vectors is proposed in this paper. Such bound constraints are dealt with by taking the advantage of the saturation characteristics of the corresponding neurons imp...
with a wide range of specified constraints. Given the difficulty, current NAS algorithms will almost always outperform manual designs. Archai can generate a gallery of architectures with specified compute characteristics. Our NAS method, Petridish, was designed with this p...
3.4. Inter-Layer Scheduling Concepts and Constraints 在顺序执行卷积层的典型硬件加速器如[6,7,19,20],部分和积累生成输出像素,然后保存到主内存,然后再次获取处理下一层,在当前的计算完成。这些输出像素,定义为中间像素和所需的权重,可以由处理引擎直接从芯片内存中获取,以计算下一层的输出。这是在[12]中首...
Normally it is a straightforward job to determine whether or not a linear network can solve a problem. Commonly, if a linear network has at least as many degrees of freedom (S*R+S= number of weights and biases) as constraints (Q= pairs of input/target vectors), then the network can ...
Neural Network Architecture refers to the structure that simulates the information processing of biological neurons, typically consisting of interconnected input, hidden, and output layers where data is processed through activation functions to produce an output, with weights updated through a learning proc...