“…He then presents a clear account of practical wisdom, emphasizing those aspects which resist mathematical modeling. Economists have attempted in the past to explain human choice based on the boundaries of practical wisdom, but this book will map the limits of those economic models…”

“…The main question to be addressed, of course, is whether mathematical continuity exists at all…”

“…Adrian Moore's historical study of the infinite covers all its aspects, from the mathematical to the mystical…”

“…Book 3 covers the mathematical basis for changing proportions of the human figure and face, while Book 4 treats the stereometry of the body as it moves in space…”

“…Key science applications require novel mathematics and mathematical models and system software that address the scalability and resilience challenges of current- and future-generation extreme-scale HPC systems. …”

“…With clear, engaging exposition, readers develop the ability to intelligently handle the more sophisticated mathematics that Fourier analysis ultimately requires…”

Tabla de Contenidos: “…4.5 Typical Applications of Tensors -- 4.5.1 Scalar -- 4.5.2 Vector -- 4.5.3 Matrix -- 4.5.4 Three-Dimensional Tensor -- 4.5.5 Four-Dimensional Tensor -- 4.6 Indexing and Slicing -- 4.6.1 Indexing -- 4.6.2 Slicing -- 4.6.3 Slicing Summary -- 4.7 Dimensional Transformation -- 4.7.1 Reshape -- 4.7.2 Add and Delete Dimensions -- 4.7.3 Swap Dimensions -- 4.7.4 Copy Data -- 4.8 Broadcasting -- 4.9 Mathematical Operations -- 4.9.1 Addition, Subtraction, Multiplication and Division -- 4.9.2 Power Operations -- 4.9.3 Exponential and Logarithmic Operations -- 4.9.4 Matrix Multiplication -- 4.10 Hands-On Forward Propagation -- Chapter 5: Advanced TensorFlow -- 5.1 Merge and Split -- 5.1.1 Merge -- 5.1.2 Split -- 5.2 Common Statistics -- 5.2.1 Norm -- 5.2.2 Max, Min, Mean, and Sum -- 5.3 Tensor Comparison -- 5.4 Fill and Copy -- 5.4.1 Fill -- 5.4.2 Copy -- 5.5 Data Limiting -- 5.6 Advanced Operations -- 5.6.1 tf.gather -- 5.6.2 tf.gather_nd -- 5.6.3 tf.boolean_mask -- 5.6.4 tf.where -- 5.6.5 tf.scatter_nd -- 5.6.6 tf.meshgrid -- 5.7 Load Classic Datasets -- 5.7.1 Shuffling -- 5.7.2 Batch Training -- 5.7.3 Preprocessing -- 5.7.4 Epoch Training -- 5.8 Hands-On MNIST Dataset -- Chapter 6: Neural Networks -- 6.1 Perceptron -- 6.2 Fully Connected Layer -- 6.2.1 Tensor Mode Implementation -- 6.2.2 Layer Implementation -- 6.3 Neural Network -- 6.3.1 Tensor Mode Implementation -- 6.3.2 Layer Mode Implementation -- 6.3.3 Optimization -- 6.4 Activation function -- 6.4.1 Sigmoid -- 6.4.2 ReLU -- 6.4.3 LeakyReLU -- 6.4.4 Tanh -- 6.5 Design of Output Layer -- 6.5.1 Common Real Number Space -- 6.5.2 [0, 1] Interval -- 6.5.3 [0,1] Interval with Sum 1 -- 6.5.4 (-1, 1) Interval -- 6.6 Error Calculation -- 6.6.1 Mean Square Error Function -- 6.6.2 Cross-Entropy Error Function -- 6.7 Types of Neural Networks -- 6.7.1 Convolutional Neural Network -- 6.7.2 Recurrent Neural Network…”

Tabla de Contenidos: “…5.6 Load Voltage Control -- 5.6.1 Control Structure and Optimal Design -- 5.6.2 Current Limiting -- 5.7 Grid‐Forming Converter Controls -- 5.7.1 Grid‐Forming Control Without a dc‐Side Capacitor -- 5.7.2 Grid‐Forming Controller with a dc‐Side Capacitor -- 5.7.2.1 Full State Feedback -- 5.8 Control Scenarios in a PV Converter -- 5.8.1 PV Voltage Control -- 5.8.2 MPPT via PV Voltage Control -- 5.8.3 Mathematical Modeling of MPPT Algorithm -- 5.8.3.1 Calculation of ddvpvppv: Method 1 -- 5.8.3.2 Calculation of ddvpvppv: Method 2 -- 5.8.4 PV Power Control -- 5.9 LCL Filter* -- 5.9.1 Passive Damping of Resonance Mode -- 5.9.2 Full‐State Feedback with Active Damping -- 5.9.3 Delay Compensation Technique Using LQT Approach -- 5.10 Summary and Conclusion -- Problems -- References -- Chapter 6 Single‐Phase Alternating‐Current (ac) DERs -- 6.1 Power Balance in a dc/ac System -- 6.1.1 Power Decoupling -- 6.2 Power Control Method via Current Feedback Loop (CFL) -- 6.2.1 Input Linearization and Feedforward Compensation -- 6.2.2 Control Structure -- 6.2.3 Calculating and Limiting Reference Current -- 6.2.4 Single‐Phase ePLL -- 6.2.4.1 Linear Analysis of ePLL -- 6.2.4.2 Two Modifications to the ePLL -- 6.2.5 Controller Formulation and LQT Design -- 6.2.6 Impact of Grid Voltage Harmonics -- 6.2.7 Harmonics and dc Control Units -- 6.2.8 Weak Grid Condition and PLL Impact* -- 6.2.8.1 Short‐Circuit Ratio (SCR) -- 6.2.8.2 LTI Model of Reference Current Generation -- 6.2.8.3 Controller and Its Design -- 6.3 Grid‐Supportive Controls -- 6.3.1 Static (or Steady‐State) Support -- 6.3.2 Dynamic (or Inertia) Support -- 6.3.3 Power Controller with Grid Support -- 6.3.4 Virtual Synchronous Machine (VSM) -- 6.3.4.1 Stability Analysis and Design of VSM -- 6.3.4.2 Start‐up Synchronization -- 6.3.4.3 Grid‐Connection Synchronization -- 6.4 dc Voltage Control and Support…”

Tabla de Contenidos: “…8.2.2 Continuous Probability Distributions -- 8.2.3 Mathematical Expectation and Moments -- 8.2.4 Return Period -- 8.2.5 Common Probability Functions -- 8.2.5.1 Binomial distribution -- 8.2.5.2 Geometric distribution -- 8.2.5.3 Poisson distribution -- 8.2.5.4 Exponential distribution -- 8.2.5.5 Gamma/Pearson Type III distribution -- 8.2.5.6 Normal distribution -- 8.2.5.7 Log-normal distribution -- 8.2.5.8 Uniform distribution -- 8.2.5.9 Extreme-value distributions -- 8.2.5.10 Chi-square distribution -- 8.3 Analysis of Hydrologic Data -- 8.3.1 Estimation of Population Distribution -- 8.3.1.1 Probability distribution of observed data -- 8.3.1.2 Hypothesis tests -- 8.3.1.3 Model selection criteria -- 8.3.2 Estimation of Population Parameters -- 8.3.2.1 Method of moments -- 8.3.2.2 Maximum-likelihood method -- 8.3.2.3 Method of L-moments -- 8.3.3 Frequency Analysis -- 8.3.3.1 Normal distribution -- 8.3.3.2 Log-normal distribution -- 8.3.3.3 Gamma/Pearson Type III distribution -- 8.3.3.4 Log-Pearson Type III distribution -- 8.3.3.5 Extreme-value Type I distribution -- 8.3.3.6 General extreme-value (GEV) distribution -- 8.4 Uncertainty Analysis -- Problems -- 9 Fundamentals of Surface-Water Hydrology I: Rainfall and Abstractions -- 9.1 Introduction -- 9.2 Rainfall -- 9.2.1 Measurement of Rainfall -- 9.2.2 Statistics of Rainfall Data -- 9.2.2.1 Rainfall statistics in the United States -- 9.2.2.2 Secondary estimation of IDF curves -- 9.2.3 Spatial Averaging and Interpolation of Rainfall -- 9.2.4 Design Rainfall -- 9.2.4.1 Return period -- 9.2.4.2 Rainfall duration -- 9.2.4.3 Rainfall depth -- 9.2.4.4 Temporal distribution -- 9.2.4.5 Spatial distribution -- 9.2.5 Extreme Rainfall -- 9.2.5.1 Rational estimation method -- 9.2.5.2 Statistical estimation method -- 9.2.5.3 World-record precipitation amounts -- 9.2.5.4 Probable maximum storm…”

“…Bioinformation publishes original research articles in all aspects of biological knowledge discovery through mathematical and computational analysis of biological data. …”

“…The development of mathematical models to predict the effects of such schemes is described and some more general problems of integrating the evaluation of interchange schemes in an urban planning context are discussed…”

“…As in the previous version, the language is kept as unobscured as possible—clarity is given priority over mathematical formalism…”

“…It finds that all markets work with four or more opposing mathematical (as opposed to physical) dimensions. It changes the way the world views economics, as it reveals the ways markets form…”

“…The book should be of interet to researchers in the field of applied mathematics, geophysics and mathematical biology…”

“…The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. …”

“…The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. …”