Recurrent neural network

randomly among the training patterns and create only that many neurons. A clustering algorithm is a kind of an unsupervised learning algorithm and is used when the class of each training pattern is not known. But an RBFN is a supervised learning network. And we know at least the class of each training pattern. So we’d better take advantage of the information of these class memberships when we cluster the training patterns. Namely we cluster the training patterns class by class instead of the entire

Comparative Predictive Modeling on CNX Nifty with Artificial Neural Network By Bikramaditya Ghosh, First and Corresponding Author Asst. Professor, ISME, Bangalore Address 301, Raghav Harmony, S R layout Off Wind Tunnel Road Bangalore-560017 INDIA E Mail- bikram77777@gmail.com Phone- +919535015777 Dr. Padma Srinivasan Assoc. Professor , Christ University, Bangalore Abstract CNX Nifty being an important barometer to indicate country’s growth has always been followed with lots

SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL NETWORKS Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org sundern@telkom.co.za theo@wpo.mlsultan.ac.za Electronic Engineering Department, M L Sultan Technikon, P O Box 1334, Durban, South Africa. ABSTRACT Crime and corruption have become rampant today in our society and countless money is lost each year due to white collar crime, fraud, and embezzlement. This

FPGA BASED IMPLEMENTATION OF DIGIT RECOGNITION Under Supervision of : Dr. Pavan Chakaraborty. Group members: IEC2012015 IEC2012028 IEC2012041 IEC2012089 IEC2012090 Table of Contents About platforms used: 4 Xilinx ISE: 4 Web Edition: 4 MATLAB: [matlab] 4 Feature extraction: 5 Algorithm speed up using FPGA implementation: 6 [parallization abitlity of NN] 6 Conclusion 7 Result: [Verilog outputs] 4 References 7 About platforms used: Xilinx ISE: “Xilinx ISE[xilinx] (Integrated Software

The objective of this review paper is to summarize and compare some of the well-known methods and application used in pattern recognition system. Keywords- Pattern recognition, classification, clustering, machine learning, error estimation, neural networks. Introduction A pattern is an entity, that could be given a name and pattern recognition is the study of how machines can observe the environment and make sense of it by differentiating between patterns. Humans are best pattern recognizers

Abstract There are numerous indications that the field of Artificial intelligence (AI) is now well established. There are several computer science departments with AI specialties and some important disciplines whose roots are in AI, such as Pattern Recognition and Symbolic Algebraic Manipulation, have already spun off as independent areas. Still, I identify this field of Artificial intelligence has the positivity beyond its aspect. It matters how we accept it and use it in a positive manner. Its

Referring to Fig. 1, mapping takes place from an input data space which may have more than 2 dimensions, onto typically a 2 dimensional array of neurons. Each neuron comprises a d dimensional weight vector (otherwise prototype vector or codebook vector) where the dimension of the input vectors is equal to d. Each neuron is connected to its adjacent neurons by a neighborhood relation, which determines the map structure or topology. The SOM can be thought of as a net which is spread to the data cloud

In[1], The techniques of artificial intelligence based on fuzzy logic and neural networks are applied together. The reasons to combine these two paradigms are because of the difficulties and inherent limitations of each isolated paradigm. They are called Neuro-Fuzzy Systems. This is often used to assign a specific type of system that integrates both of these techniques. This is characterized by a fuzzy system where fuzzy sets and rules are adjusted using input output patterns. There are many different

Analysis of Attentiveness using Physiological and Environmental Factors Abstract—Wearable computing is picking up speed and devices like smart watches and fitness bands are increasingly equipped with heart rate sensors. Common applications for these devices include fitness and sleep tracking. Heart rate sensor data opens avenues for exploring newer applications. There is a close correlation between attentiveness and the variability in heart rate in adults. In this paper, we utilize this correlation

Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks promotes