IRIS DATA ANALYSIS USING BACK PROPAGATION NEURAL NETWORKS Sean Van Osselaer Murdoch University‚ Western Australia ABSTRACT This project paper refers to experiments towards the classification of Iris plants with back propagation neural networks (BPNN). The problem concerns the identification of Iris plant species on the basis of plant attribute measurements. The paper outlines background information concerning the problem‚ making reference to statistics and value constraints identified
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variables AGE1‚ AGE2‚ CHANGEM‚ CHANGER‚ DIRECTAS‚ MOU‚ OVERAGE‚ RECCHRGE‚ REVENUE and ROAM. 3. Run at least 6 models on SAS - Decision Trees (binary and three way tree)‚ Logistic Regression‚ Logistic Regression with Transform Variables‚ Neural Networks‚ Neural Networks after selection of variables/ transform variables). Initial Data Preparation 1. Partitioning the data The data needs to
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Robust Fixed-Point Algorithms for Independent Component Analysis Aapo Hyvärinen Helsinki University of Technology Laboratory of Computer and Information Science P.O. Box 5400‚ FIN-02015 HUT‚ Finland Email: aapo.hyvarinen@@hut.fi IEEE Trans. on Neural Networks‚ 10(3):626-634‚ 1999. Abstract Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this
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generic systems. Neural networks‚ or connectionist models‚ are the antithesis of knowledge-based approaches in that they are extremely adept at generalising which gives them the ability to work with very noisy data. The research project described in the paper employs both knowledge-based representations and neural networks to model students using non-domain specific parameters‚ such as browse strategies and ability to answer questions. The domain is structured in a hypermedia network using semantic
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intelligence (AI). AI technologies try to work the way the human brain works‚ by making intelligent guesses‚ learning by example‚ and using deductive reasoning. Some of the more popular AI methods used in data mining include neural networks‚ clustering‚ and decision trees. Neural networks look at the rules of using data‚ which are based on the connection found or on a sample set of data. As a result‚ the software continually analyses value and compares it to the other factors‚ and it compares these factors
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Demand Forecasting Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods‚ such as educated guesses‚ and quantitative methods‚ such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions‚ in assessing future capacity requirements‚ or in making decisions on whether to enter a new market.
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History of Artificial Intelligence The history of artificial intelligence (AI) began in antiquity‚ with myths‚ stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes‚ AI began with "an ancient wish to forge the gods." The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable
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Phan‚ T.J. Gale‚ Fuzzy Sets Syst. 159 (2008) 871–899. [31] A. Kamalabady‚ K. Salahshoor‚ J. Process Control 19 (19) (2009) 380–393. [32] G.O. Guardabassi‚ S.M. Savaresi‚ Automatica 37 (1) (2001) 1–15. [33] K.S. Narendra‚ S. Mukhopadyay‚ IEEE Trans. Neural Netw. 8 (3) (1997) 475–485. [34] L. Ljung‚ T. Soderstrom‚ MIT Press‚ London‚ 1983. [35] P.P. Angelov‚ FilevF D.P.‚ IEEE Trans. Syst. Man Cybern. B: Cybern. 34 (1) (2004). [36] Babuska‚ H. Verbruggen‚ Annu. Rev. Control 27 (2003) 73–85. [37] K. Salahshoor
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QUESTION1 Power Problems Alternating current (AC)‚ which is “food” to PCs and other network devices‚ is normally 110 volts and changes polarity 60 times a second (or 60 Hertz). These values are referred to as line voltage. Any deviation from these values can create problems for a PC or other network device. Power problems fall into three categories: * Overage * Underage * Quality Power Overage Problems During a power overage‚ too much power is coming into the computer. Power overage
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Networks During the Program Student Worksheet 1 What are networks? 1. Describe the purpose of networking computers. 2. Describe the two types of network: a. LAN b. WAN 3. List the core business functions of Destra. 4. As a retailer‚ describe how Destra is different to more traditional retailers. 5. What does Destra’s network consist of? 6. What is VPN and what is its purpose? 7. Describe
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