Neural network fuzzy systems 5.4 Apk

The app is a complete free handbook of Neural network, fuzzy systems which cover important topics, notes, materials, news & blogs on the course. Download the App as a reference material & digital book for Brain and Cognitive Sciences, AI, computer science, machine learning, knowledge engineering programs & degree courses.  This useful App lists 149 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 10 chapters. The app is must have for all the engineering science students & professionals.  The app provides quick revision and reference to the important topics like a detailed flash card notes, it makes it easy & useful for the student or a professional to cover the course syllabus quickly before an exams or interview for jobs.  Track your learning, set reminders, edit the study material, add favorite topics, share the topics on social media.  You can also blog about engineering technology, innovation, engineering startups,  college research work, institute updates, Informative links on course materials & education programs from your smartphone or tablet or at http://www.engineeringapps.net/.  Use this useful engineering app as your tutorial, digital book, a reference guide for syllabus, course material, project work, sharing your views on the blog.  Some of the topics Covered in the app are: 1) Register Allocation and Assignment 2) The Lazy-Code-Motion Algorithm 3) Matrix Multiply: An In-Depth Example 4) Rsa topic 1 5) Introduction to Neural Networks 6) History of neural networks 7) Network architectures 8) Artificial Intelligence of neural network 9) Knowledge Representation 10) Human Brain 11) Model of a neuron 12) Neural Network as a Directed Graph 13) The concept of time in neural networks 14) Components of neural Networks 15) Network Topologies 16) The bias neuron 17) Representing neurons 18) Order of activation 19) Introduction to learning process 20) Paradigms of learning 21) Training patterns and Teaching input 22) Using training samples 23) Learning curve and error measurement 24) Gradient optimization procedures 25) Exemplary problems allow for testing self-coded learning strategies 26) Hebbian learning rule 27) Genetic Algorithms 28) Expert systems 29) Fuzzy Systems for Knowledge Engineering 30) Neural Networks for Knowledge Engineering 31) Feed-forward Networks 32) The perceptron, backpropagation and its variants 33) A single layer perceptron 34) Linear Separability 35) A multilayer perceptron 36) Resilient Backpropagation 37) Initial configuration of a multilayer perceptron 38) The 8-3-8 encoding problem 39) Back propagation of error 40) Components and structure of an RBF network 41) Information processing of an RBF network 42) Combinations of equation system and gradient strategies 43) Centers and widths of RBF neurons 44) Growing RBF networks automatically adjust the neuron density 45) Comparing RBF networks and multilayer perceptrons 46) Recurrent perceptron-like networks 47) Elman networks 48) Training recurrent networks 49) Hopfield networks 50) Weight matrix 51) Auto association and traditional application 52) Heteroassociation and analogies to neural data storage 53) Continuous Hopfield networks 54) Quantization 55) Codebook vectors 56) Adaptive Resonance Theory 57) Kohonen Self-Organizing Topological Maps 58) Unsupervised Self-Organizing Feature Maps 59) Learning Vector Quantization Algorithms for Supervised Learning 60) Pattern Associations 61) The Hopfield Network 62) Limitations to using the Hopfield network Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding.  Neural network, fuzzy systems is part of Brain and Cognitive Sciences, AI, computer science, machine learning, electrical, electronics, knowledge engineering education courses and technology degree programs at various universities.