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The MATLAB code that accompanies Missile Flight Simulation cannot be downloaded from this website. Please request the code by e-mail at jeff@humalytica.com.

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The Models that accompany Discrete Event Simulation Using ExtendSim 8 cannot be downloaded from this website. Please request the Models by e-mail at jeff@humalytica.com.

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Neural Networks using R

Neural Networks using R By Jeffrey Strickland on May 13, 2015 The intent of this article is not to tell you everything you wanted to know about artificial neural networks (ANN) and were afraid to ask. For that you’ll have to ask someone else. Here I only intend to tell you how you might use R to implement an ANN model. One thing I will say is that I rarely use an ANN. I have found them to work best in an ensemble model (using averaging) with logistics regression models. Using neuralnet neuralnet depends on two other packages: grid and MASS ( Venables and Ripley, 2002). It is used is primarily with functions dealing with regression analyses like linear models ( lm ) and general linear models ( glm ). As essential arguments, we must specify a formula in terms of response variables ~ sum of covariates and a data set containing covariates and response variables. Default values are defined for all other parameters (see next subsection). We use the data set infert that i

Time Series Analysis using iPython

Time Series Analysis using iPython In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at http://statsmodels.sourceforge.net/stable/index.html . Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) can be called from the tsa (Time Series) module from the Statamodels package. Note: I am not as expert in time-series analysis as I am in other areas of Analytics, so if you find errors I would be happy to know about them and correct them. Introduction ARIMA models are, in theory, the most general class of models for forecasting a time series, which can be made to be “stationary” by differencing (if necessary), perhaps in conjunction with nonlinear transformations such as logging or deflating (if necessary). A random variable that is a time series is stationary if its statistical properties are all constant over time. A stationary series has no trend

Clouds, clouds, and more clouds

Clouds, clouds, and more clouds There are at least eleven kinds of clouds: cirrus, cirrocumulus, cirrostratus, altocumulus, altostratus, cumulonimbus, cumulus, nimbostratus, stratocumulus, small Cu, and stratus. But this article is not about those kinds of clouds. Of course there are other kinds of clouds, like iCloud, Google Cloud, Azure Cloud, Amazon Cloud, and the list goes on. But this article is not about those clouds either. This article is about text analytics . Clouds and Text Analytics The picture above was generated by R as a Word Cloud . It is equivalent to a frequency distribution (see Figure 1), where the size of the characters comprising a word corresponds to its frequency count, so that the word “icloud” occurs many times in the text, while the word “people” (inside the “d” in “icloud”) occurs less frequently. In Figure 1, “speed” occurs most frequently and is a key concept in flight dynamics, and in corresponding models. Figure 1. Frequency distrib

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