Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit nptel.iitm.ac.in
charles.underground.net This is a demonstration of a neural network imitator script. Impulses change which synapses become stronger and weaker, effectively making the system "learn". Complex loops can be created and snuffed out, creating an interesting simulation.
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The simulation is based on a neural network to guide the car around the track and a genetic algorithm used to pick and breed the most succesful cars of each generation. For the people who are interested in how it works: The car uses its 5 feelers to gauge a normalised intersection depth with the track's edges and then feeds this 5 values as inputs to the neural network. The inputs are then passed through a hidden layer of 8 neurons and then finally to an output layer of 2 neurons: a left and right steering force. These forces are then used to drive the car forward and turn the car. Each car represents a different genome in a generation (or a unique set weights for the neural net) which are evaluated and potentially carried through to the next generation by a fitness score. The fitness score is based on the distance along the track achieved as well as bonus points for hitting checkpoints (this encourages the network to navigate around tricky sections of the track). In the video, the neural network / genetic algorithm finds a solution at about 2.25. FRAPs interfered a little by clamping the framerate, so it took a bit longer than usual for it to find a solution. Legend: The green dots are the intersection points of the feelers. The blue lines are the feelers The orange highlighted track geometry is track sections that are being considered for collision. The blue circle is the radius used to search for track nearby track geometry.
xoax.net This neural networks video lesson demonstrates how a single-layer perceptron works, is programmed, and can be used to analyze data. Please submit all questions to our forum: xoax.net
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DISCLAIMER: This video is boring to the average YouTube viewer. Basically this was an assignment for a class, wanted to get it up on YouTube in case the TA wanted to see it. In the rare case anyone cares to see this video, these X-Wings (the only free model I could find that wasn't ugly as hell) are controlled by a neural network. 4 inputs, a hidden layer of 3 nodes, and 4 output nodes. I could reduce the outputs down to 2, but it's too late for that now (I'll do it in the next project).
My final project for my Intro to Artificial Intelligence class was to describe as best as I can one concept from Artificial Intelligence. I chose Neural Networks because they are one of the better known AI concepts, but are still very poorly understood by most people. Leave me a comment if you like, or email me a question directly at jesussuarez@tamu.edu. Thanks. For the middle school students reviewing these projects, the survey link is: www.surveymonkey.com
This program simulates an environment in which simple organisms with brains can evolve. The brains are directed graphs with nodes as the neurons and edges as axon/dendrite pairs. The neural network is simulated fully with each signal traveling down the edges, instead of having the conventional approach where all neurons are updated per simulation tick. The DNA is composed of: the structure of the brain, (ie the set of existing edges) parameters for neurons (firing threshold, excitation decay rate), and edges (speed, strength) Genetic algorithm with sexual reproduction is used to find brains that maximize the number of simulation ticks that an organism survives. Programmed in Python, Pygame library is used for drawing.
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Learn Neural Net Programming: www.heatonresearch.com Introduction to Neural Networks with C# is a course that introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures will be presented. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples will be examined that include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.
Jeff Heaton Heaton Research Neural Network Artificial Intelligence Hopfield Self Organizing Map Online Course C# genetic algorithm simulated annealing pattern recognition Heaton Research
From www.heatonresearch.com. A simple introduction to how to represent the XOR operator to machine learning structures, such as a neural network or support vector machine.
gesture recognition using a kinect an a neural network. We using the openNI and the NITE librairies with a Kinect to track body parts (hands, elbow, head, ...). Then we use FANN (Fast Artificial Neural Network) library for the recognition system. For now, It is able to recognize only two moves, one means 'hello' ("bonjour" in franch), the other mean 'sorry' ("désole" in french). We just need to record new moves to train the neural network to be able to recognize other "words"/moves.
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Go to www.nwtai.com for more information - this is a newly incorporated company. This video is a neural network based stock price prediction tool that is currently in development (latter stages). The algorithm is implemented via Matlab scripts (Matlab version 5.2). More testing and implementation of a GUI will be completed by Sept. 15th, 2010. A Java front end is also being built simultaneously for interfacing with various stock internet portals.
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Support the show! tymkrs.com For more information on this project, visit us on our new forums! www.tymkrs.com You can talk to us daily at our IRC chat (#tymkrs on AfterNet): qwebirc.afternet.org zombietech.tv Back by popular demand, our homebrew neural network in a new package. This time we are teaching you guys about how simple individual AI can work together to solve complex problems. For more information on the inside jokes, check out: www.thegeekgroup.org www.jeriellsworth.com benheck.com www.thegeekgroup.org www.missmooseart.com Thanks for all of the comments so far, feedback from you guys helps us stay motivated! If you want to get the blow by blow on the production of these videos be sure to follow us on Twitter twitter.com Don't hesitate to message us! Attribution: Ant on a tree, by Thomas Quaritsch, 2005-07-22 The Toymaker, by Chris Newberg, 2010-09-20
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"The task of this neural net is to span its map over all blue points (the input values of the net) in an even way and without crossing one point twice. This problem is similar to the classic Travelling Salesman Problem, where the shortest path between a certain number of cities is to be found without passing one city twice. Using conventional methods, like the backtracking algorithm, this problem can't be solved on any computer in time for a number of cities greater than about 25" To learn more about neural networks or to see the applet look yourself at: fbim.fh-regensburg.de
My first attempt in neural networks. I connected 1 adruino mega, 3 servos, 1 analog infrared receiver, 3 contacts and 2 encoders from an old printer. I use a genetic algorithm that trains the 5-7-2 neural network (5 inputs, 7 neurons in the hidden layer and 2 in output). The 5 inputs are the 5 areas that the IR scans using the third servo, and the 2 outputs are the speed of each wheel servo. More info in greek language here: www.grobot.gr Enjoy :)
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Genius Features™: Artificial Intelligence modules Proprietary genetic algorithms Build powerful new tools using Wave59 indicators as building blocks & make familiar techniques more powerful. Indicators like Moving Averages and Momentum Curves are more effective after theyve been processed with Wave59s Neural Network wizard. Wave59 contains an advanced Artificial Intelligence module that allows technical traders to apply neural networks to a specific problem, such as reducing lag or even forecasting indicator values 2-3 bars into the future. Wave59s proprietary genetic algorithm teaches the neural network the patterns in the data and has complete control over all phases of network training and structure. Genetic algorithms are optimization tools based on evolutionary theories - chromosomes containing neural network data are mated, mutated, and sorted in the same way as plant and animal life in nature. The best chromosome at the end of the training period is the one that gets used in the neural network.
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cg.skeelogy.com The enemy robots in this mini-game uses neural networks (NN) to do decision making. Prior to what you see here, the NN has undergone some training so that it has learnt how to react based on a human player's reactions. Notice these things about the trained robots in the video: i) They approach me when I'm near and stop to attack when they are in shooting distance. They approach me again when I back away. ii) Once their HP is low, they know that they should flee instead of trying to attack again iii) Although they have started to flee, they try to attack me again when I'm not facing them. Once I point the cursor at them, they know that they should flee, even though I haven't even started shooting. This implies that they are especially good at back attacks. And all these are done by simply training the neural networks. Note: The fleeing in a zig-zag manner is procedural and not done by the neural networks.
Jonathan McCabe's artificially evolved systems art "The patterns are made by repeated foldings, rotations and shifts, and then each point is coloured depending on its positions during the operation. A process of artificial evolution was employed to develop the final images, involving repeated variation, selection and "cross breeding" of the recipes used to generate the images." sf.anu.edu.au
I designed and created this robot (Lola) for my a senior design class. I wanted to look at learning and control so I choose to use an Artificial Neural Network (ANN) as my controller/brain.The way it worked was that the robot has six sensors. These sensors fed into the input layer of the ANN. The information propogated through the network and would come out of the output nodes. The output nodes would then control the speed ad direction of the main servos controlling the arm. In order to find the right weights and topologies to get the ANN to read in the sensors and then actuate the servos to produce crawling behavior, I used an genetic algorithm to evolve an ANN. The genetic algorithm I used is called NEAT. I read about it from a paper called "Competitive Coevolution through Evolutionary Complexificaition" by Kenneth Stanley. If you search in google the first link will take you to the paper. I plan on putting up at least one more video which shows how the ANNs are producing the crawling behavior. If anyone knows of a free and easy to use movie maker that allows me to play two movies side by side please let me know. But please comment or send me messages and tell me what you guys think. Update (6/5/10) Here's a link to the paper. If you guys like it and want to use something from it please ask and/or cite. I'm more than happy to talk to people about it and see the work continued. www.engin.swarthmore.edu/e90/2008/reports/Roby%20Velez.pdf
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This video reviews the basic architecture of the Encog ( www.encog.org ) Artificial Intelligence Framework. The major object types of layers, synapses, and neural logic are reviewed. Feedforward, Elman and Hopfield neural networks are demonstrated.
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Artificial neural network driven mobile robots learn how to drive on roads in simulation. The neural networks are evolved using Ken Stanley's HyperNEAT algorithm. Computational Intelligence Group Czech technical University in Prague 2008
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Playing with neural networks classification using Sharky Neural Network (SNN). This free software is available on page: www.SharkTime.com Two-spirals problem is often used as a test for comparing the quality of different learning algorithms. Two different neural networks you can see in this movie (2 and 2:5:10:2). SNN is using supervised machine learning with backpropagation algorithm. Each neuron has bias and bipolar sigmoid activation function. Voiced by Ivona www.ivona.com Music by Włodzimierz Antoniw www.antoniw.eu Classification neural network in action www.SharkTime.com
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xoax.net This lesson demonstrates how a Probabilistic Neural Network works, is programmed, and can be used to analyze data. Please submit all questions to our forum: xoax.net
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RoboSight is a new neural network based camera that is compatible with lego mindstorms and most robots and microcontrollers. It can be trained using on-board controls or using a serial connection. For more info see: www.rnisys.com
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This is video capture of Polyworld program running. I've surprisingly managed to install it on Ubuntu and as you see it does its job. Polyworld is computer simulation of creatures that can see their surrounding, can process information using random recurrent neural networks and according to its properties and "experiences" make decisions. You can download Polyworld here: sourceforge.net
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Quick and dirty neural nets simulation. I'm pretty sure i didn't get the physics of the neural networks right, and i will work on this more later, but it's pretty cool to watch still :) Here is a sample of the program... this is roughly what i imagine our brain cells work like :) organized chaos
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This is a Elman Simple Recurrent Network with 5 context layers (Tower Network) and 30 hidden nodes and a bias node for input and in the hidden layer going to the output. This network was trained on this level, using a genetic algorithm with elitism turned on.
Support the show! tymkrs.com You can talk to us daily at our IRC chat (#tymkrs on AfterNet): qwebirc.afternet.org For more information on this project, visit us on our new forums! www.tymkrs.com zombietech.tv Today we are exploring what can be done by using simple cellular automata combined with co-dominant genetics to procedurally generate flora that responds and adapts to the environment. We are taking it one step further, letting a simple neural network based grazing fauna type life form loose in the simulation. Voice over by @atdiy Code and Music by @whixr If you want extra daily content about us, our projects, and the production of these videos be sure to follow us on Twitter, Tumblr, and Flickr. You can always find links to all of our stuff at about.me or individually at these pages: tymkrs.com twitter.com www.flickr.com tymkrs.tumblr.com We love hanging out with you guys on Twitter while working on our projects and these videos. Life is so much more fun with good company. Don't hesitate to message us! As you can see, we do read them, and do our best to answer! If you would like to help support our ability to make many more programs like this one, please share this video. We need you! Puff Puff Pass is trending on Twitter instead of something worthwhile. Come on geeks!!! Tweet and ReTweet - it costs you nothing and helps save future generations from becoming idiots.
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SharpNEAT evolves a neural network. NEAT simultaneoulsy evolves both network structure (topology) and connection weights.
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Neural Networks is a sophisticated modeling tool, capable of modeling very complex relationships in data. This episode explores the application of neural networks in a regression problem using STATISITCA Automated Neural Networks. The options used for regression are similar for other neural networks applications such as classification and time series. The episode explores various analysis options and demonstrates working with neural network output. statsoft.com
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From www.heatonresearch.com. In this part we see how to present data to a neural network to predict data.
A project I did to train neural networks to play Puyo Puyo. I needed a platform to test the agents on as well, so I wrote the simple Puyo Puyo clone you see here. This demo shows two agents competing after being trained for about 20 generations.
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A little artificial intelligence project of mine which was written in C# with XNA Game Studio 3.0. The tanks in the simulation are autonomously controlled using an their own artificial neural networks. Instead of traditional back propagation for learning an evolutionary algorithm is used to adjust weights. A blue tank uses a genetic algorithm and the red tanks use particle swarm optimization. The video shows snap shots of some of the generations which last for just over a minute or so. Eventually during the later generations over fitting occurs and performance gain is negligible.
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Support the show! tymkrs.com You can talk to us daily at our IRC chat (#tymkrs on AfterNet): qwebirc.afternet.org www.zombietech.tv Here is a quick introduction to fancy mice, animal husbandry, genetics, and how friendly little pets like these can help researchers develop new technologies. This video was filmed by suspending a FlipUltraHD video camera upside down mounted on a mono-pod hanging from an aluminum bar bridging the top of the tank. The voice over was recorded in our studio with an M-Audio small diaphragm condenser microphone. The background music is an original composition, toned down a bit to blend in well. The photograph of the agouti house mouse, and of the mouse pup were both taken with a Nikon D40 and a macro filter. Our mice enjoy a natural diet. We only use sustainable fast growth soft wood bedding, which we later compost for our garden. Thanks for all of the comments so far, feedback from you guys helps us stay motivated! And a big welcome to all of our new subscribers, we're tickled that you're tickled. If you want to get the daily blow by blow on theproduction of these videos be sure to follow us on Twitter, Tumblr, and Flickr. We've already connected with many of you, and look forward meeting more. You can always find links to all of our stuff at about.me or individually at these pages: twitter.com www.flickr.com tymkrs.tumblr.com Don't hesitate to message us! If you would like to help support our ability to make many more programs like this one <b>...</b>
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A video of Cortex7 beta application being tested during development. This video doesn't have any particular structure, it just shows off the GUI. I enable training for SUN and the network starts to show average gains greater than random chance. While the SUN network is training a new data set (MSFT) is downloaded off the net and added to the workspace. In the next video I will give a walk through of setting up, training and evaluating a network for a particular ticker symbol. Cortex7 forum: www.cortex7.net Cortex7 homepage www.ledset.com
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PLEASE COMMENT! This is my theory about the global intelligence, call it god or whatever you like it. it is just a thought that struck me watching all those documentaries about religion networks and the way our brain works. I could be totally wrong with this, but still, what if... So i made this slideshow to explain what i think of it because now i can explain my theory in 5 minutes instead of talking for hours:) All the pictures are real and found on the internet,,,, Im writing a book about it right now, but it will take some time before i finished. It will be a free downloadable ebook for the moment, But maybe if i get enought response i will also try to find a publisher... You can also download the videofile here rapidshare.com It is a mpeg4 avi and should play on al modern dvd players. but you can also put it on your site if you want to. -------------- ABOUT THE MUSIC IN THIS VIDEO... The music was done by me in Propellerheads Reason 2.5 If you would like to hear some more you can download it here for free. Electroflux is electrohouse, Core suite is an Electro concept album and Synsperiment is the experimental music that you hear in this video. The track is called Stufe 4 Geding... All done by me in Reason. Download link. rapidshare.com enjoy...
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Learn Neural Net Programming: www.heatonresearch.com Hopfield networks are simple neural networks invented by John Hopfield. This video provides a basic introduction to using Hopfield networks with the Encog Artificial Intelligence framework.
Jeff Heaton Heaton Research Neural Network Artificial Intelligence Hopfield pattern recognition Encog Heaton Research
