Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition

By Sandhya Samarasinghe. 2006

523 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4106d27af99f7ab54e2a1744841a7620c2c2ae31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Majeed Safa
2. M. Nejat
3. Gouhei Tanaka
4. Toshiyuki Yamane
5. Jean Benoit Héroux
6. Ryosho Nakane
7. Naoki Kanazawa
8. Seiji Takeda

Citations in the corpus

• Recent Advances in Physical Reservoir Computing: A Review (2018, background): “Artificial neural networks (ANNs) constitute the core information processing technology in the fields of artificial intelligence and machine learning, which have witnessed remarkable progress in recent years, and they are expected to be increasingly employed in real-world applications (Samarasinghe,”
• Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand (2015, methodology): “PCA uses the mean and variance of each input variable and the covariance between variables to create a covariance matrix (COV) (Samarasinghe, 2007) and transforms the COV to obtain independent components that are linear summations of the original inputs.”