Learned Probabilistic Prediction in a Weightless Neural Network
نویسنده
چکیده
This paper examines a weightless neural network traind to perform a probabilistic, iconic prediction task. The paper discusses both the network architecture and training scheme used. The iconic prediction task is examined both with and without a controlling input. Finally some speculative parallels are drawn between the system behaviour and prediction in biological systems.
منابع مشابه
Probabilistic automata simulation with single layer weightless neural networks
Computability of weightless neural networks is the major topic of this paper. In previous works it has been shown that, one can simulate a Turing machine with a weightless neural network (WNN) with an infinite tape. And it has also been shown that one can simulate probabilistic automata with a WNN with two queues. In this paper, we will show that is possible to simulate a probabilistic automata...
متن کاملArtificial Intelligence for prediction of porosity from Seismic Attributes: Case study in the Persian Gulf
Porosity is one of the key parameters associated with oil reservoirs. Determination of this petrophysical parameter is an essential step in reservoir characterization. Among different linear and nonlinear prediction tools such as multi-regression and polynomial curve fitting, artificial neural network has gained the attention of researchers over the past years. In the present study, two-dimensi...
متن کاملTridimensional Pattern Reconstruction by Using Weightless Artificial Neural Networks
1. ABSTRACT This paper describes the structure and behavior of a system, composed by a set of weightless artificial neural networks, which is capable of learning different images and then reconstructing an image according to the closest learned pattern. This paper presents a technique which considers Hamming distance for pattern learning and reconstruction, therefore it is posible to study the ...
متن کاملImplementation of Probabilistic Automata in Weightless Neural Networks
The objective of this paper is to analyze the practical viability of the results theoretical concerning the relationship of a class of weightless neural networks, known as General Single-layer Sequential Weightless Neural Networks (GSSWNNs), and Probabilistic Automata (PA). This study was based on the theoretical model development by de Souto [1]. This model shows the computational equivalence ...
متن کاملAn Analysis of Hardware Configurations for an Adaptive Weightless Neural Network
This paper examines the potential offered by adaptive hardware configurations of a class of weightless neural architecture called the Enhanced Probabilistic Convergent Network targeted on a Virtex-II pro FPGA which is re configurable. The reconfiguration and adaptive capability of the Enhanced Probabilistic Convergent Network is a highly adaptive architecture offering a very fast, automated, un...
متن کامل