Multilevel Neural Network for Reducing Expected Inference Time
نویسندگان
چکیده
منابع مشابه
Neural Network Capacity for Multilevel Inputs
Matt Stowe and Subhash Kak . Abstract: This paper examines the memory capacity of generalized neural networks. Hopfield networks trained with a variety of learning techniques are investigated for their capacity both for binary and non-binary alphabets. It is shown that the capacity can be much increased when multilevel inputs are used. New learning strategies are proposed to increase Hopfield n...
متن کاملNeural network structure inference from its time series
I explore the task of neural network connectivity (i.e. graph) inference from a set of temporal measurements at each neuron (nodes). In this work, all experiments are based on numerically simulated neuronal populations, with known ground-truth graph structures. I develop a statistically principled framework for edge inference based on the concept of “false discovery rate” (FDR) as outlined in R...
متن کاملAdaptive fuzzy inference neural network
An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean...
متن کاملDetermining the importance of soil properties for clay dispersibility using artificial neural network and daptive neuro-fuzzy inference system
The main purpose of the current research is comparing the results of Artificial Neural Network (ANN) with Adaptive Neuro-Fuzzy Inference System (ANFIS) with regard to determination of the importance of soil properties affecting clay dispersibility. After taking samples from two depths of 0-40 and 40-80 cm, the spontaneous and mechanical dispersions of clay were recorded using both weighing and ...
متن کاملVehicle's velocity time series prediction using neural network
This paper presents the prediction of vehicle's velocity time series using neural networks. For this purpose, driving data is firstly collected in real world traffic conditions in the city of Tehran using advance vehicle location devices installed on private cars. A multi-layer perceptron network is then designed for driving time series forecasting. In addition, the results of this study are co...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2952577