Computer-aided learning in artificial neural networks
نویسندگان
چکیده
This paper describes the development and evaluation of a computer-aided learning (CAL) package for a graduate course in artificial neural networks (ANNs). The package has been evaluated over a period of two academic years, both as an educational supplement to a conventional lecture course and as a completely self-sufficient, remotely taught course. The course is accessed via the World Wide Web (WWW). The course features Java applets for animation/demonstration purposes, employing the MATLAB computational engine for interactive examples and assignments. In an effort to provide a classroom-like environment, an interactive discussion forum is provided, along with weekly lecture summaries from the conventional lecture course. Automatically marked question pools are available for self-assessment.
منابع مشابه
On the convergence speed of artificial neural networks in the solving of linear systems
Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper is a scrutiny on the application of diverse learning methods in speed of convergence in neural networks. For this aim, first we introduce a perceptron method based on artificial neural networks which has been applied for solving a non-singula...
متن کاملSolving Fuzzy Equations Using Neural Nets with a New Learning Algorithm
Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...
متن کاملSolving Fuzzy Equations Using Neural Nets with a New Learning Algorithm
Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...
متن کاملPrediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database...
متن کاملSolving nonlinear Lane-Emden type equations with unsupervised combined artificial neural networks
In this paper we propose a method for solving some well-known classes of Lane-Emden type equations which are nonlinear ordinary differential equations on the semi-innite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feed-forward neural networks cont...
متن کاملINTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE Trans. Education
دوره 45 شماره
صفحات -
تاریخ انتشار 2002