20.1 Definitions and Scope Introduction. Definitions and Style of Computation. ANN Types and Applications 20.2 Multilayer Perceptrons Function of Each PE. How to Train MLPs Applying Back- Propagation in Practice. A Posteriori Probabilities 20.3 Radial Basis Function Networks 20.4 Time Lagged Networks Memory Structures. Training-Focused TLN Architectures

• Why need networks • History of Networks • Network Classification • Layered Network Model

In this chapter we shall investigate some ways of characterizing two-port networks. Before we do this, we must consider some of the more general details that apply to all networks which have two port. In addition, we shall consider interconnec of two-

Uncertainty Probability Syntax and Semantics Inference Independence and Bayes’ Rule Bayesian network Graphical models Bayesian networks Inference in Bayesian networks

1.206J/16.77J/ESD. 215] Airline Schedule planning Outline Sign-up Sheet syllabus The Schedule planning process Flight Networks Time-line networks · Connection networks

1.1 What is the Internet? 1.2 Network edge 1.3 Network core 1.4 Network access and physical media 1.5 Internet structure and ISPs 1.6 Delay & loss in packet-switched networks 1.7 Protocol layers, service models 1.8 History

复旦大学：《计算机网络 Computer Networking》课程电子教案（PPT课件讲稿）26 Wireless, Ad-Hoc Networks, Sensor Networks

1. Physical layer. (Encoding) 2. Datalink layer introduction, framing, error coding, MAC, switched networks. 3. Broadcast-networks, home networking

Lecture Outline A fundamental result for queuing networks State transition diagrams for Markovian queuing systems and networks: example Analysis of systems with dynamic demand and service rates Qualitative behavior of dynamic systems

Review: Constraint-based Interval Planning Simple Temporal Networks Schedulability and Scheduling Review: Temporal, Model-based Programming Managing Execution Uncertainty Execution with Dynamic Scheduling Execution with Explicit Models of Uncertainty