Commonly used algorithms
Embedded artificial intelligence often needs to consider resource-constrained environments, so choosing algorithms suitable for embedded systems is crucial. Here are some commonly used algorithms in embedded artificial intelligence:
卷积God经网络（CNN）：
Used for computer vision tasks such as image recognition and object detection.
Lightweight network structures are usually used in embedded systems, such as MobileNet and SqueezeNet.
Recurrent Neural Network (RNN) and Long Short-Term Memory Network (LSTM< a i=4>):
Suitable for sequence data, such as speech recognition and natural language processing.
Some simplified versions may be used in embedded systems, or more efficient variants such as GRU (Gated Recurrent Unit) may be used.
Support Direction Machine (SVM):
Used for classification and regression tasks, especially in the field of pattern recognition.
Suitable for embedded systems, especially when resources are limited.
Decision trees and random forests:
Used for classification and regression tasks, with good interpretability.
Model size can be reduced in embedded systems by limiting the depth and number of nodes of the tree.
KKinkai Arithmetic:
Used for pattern recognition, classification and regression tasks.
It can be used in embedded systems, but you need to pay attention to the memory usage.
Clustering Algorithm:
Such as K-means clustering, which is used to divide data into different categories.
It can be used for data analysis and pattern recognition in some embedded systems.
Reinforcement learning algorithm:
In some specific embedded applications, such as intelligent control systems, reinforcement learning algorithms can be used.
Sparse encoding:
Used for feature extraction and dimensionality reduction, helping to reduce model size.
Can be used to save computing resources in embedded systems.
Bayesian network:
Used to deal with uncertainty and suitable for decision-making problems in some embedded systems.
When selecting an algorithm, factors such as model performance, complexity, interpretability, and resource consumption in embedded systems need to be comprehensively considered. At the same time, for some specific embedded applications, it may be necessary to specifically design and optimize algorithms.
convolutional neural network
Convolutional Neural Network (Convolutional Neural Network, CNN) is a type of Deep learning models with grid-like structured data. CNN has achieved great success in the field of computer vision and is widely used in tasks such as image recognition, object detection, and image generation.
main feature
卷积层（Convolutional Layer）：
The core of CNN is the convolution layer, which can effectively extract features from the input data and retain spatial structure information through convolution operations.
池化层（Pooling Layer）：
The pooling layer is used to reduce the spatial dimension of the feature map, reduce computational complexity, and make the model more robust to small changes in position.
Activation Function (Activation Function):
Activation functions (such as ReLU) are embedded in the convolutional layer, introducing nonlinearity and helping the model learn complex mapping relationships.
Fully Connected Layer (Fully Connected Layer):
On top of the output of the convolutional layer, a fully connected layer is usually connected to learn global information and perform final classification or regression.
Weight sharing:
The convolutional layer processes inputs at different locations by sharing weights, reducing the number of model parameters and improving the statistical performance of the model.
CNN Basic structure
Input Layer (Input Layer):
Receives raw input data, such as images.
卷积层（Convolutional Layer）：
Use a convolution kernel to perform a convolution operation on the input and extract features.
Activation Layer (Activation Layer):
Apply a nonlinear activation function, such as ReLU, to the convolutional layer output.
池化层（Pooling Layer）：
Reduce the spatial size of feature maps.
Repeat multiple times:
Convolutional layers, activation layers, and pooling layers are repeatedly stacked to form a deep network structure.
Fully Connected Layer (Fully Connected Layer):
Map high-level features to output categories.
Application areas
Image classification:
Identify objects or scenes in images.
Object detection:
Locate the location of multiple objects in an image.
Semantic segmentation:
Assign each pixel in the image to a specific category.
Face recognition:
Recognize faces in images or videos.
Image generation:
Generate new images, such as image super-resolution, style transfer, etc.
The advantage of CNN is that it can effectively capture the local characteristics of the input data, so it is particularly suitable for processing data with a grid structure, such as images.
Recurrent Neural Networkand Long Short-term Memory Network
Circular neural network (RNN ) and long -time memory network ( lstm ) are deep learning An important type of network used in processing sequence data. They have achieved remarkable success in fields such as natural language processing, speech recognition, and time series analysis.
Cycling God 经网络（RNN）
basic structure
RNN is a recurrent neural network whose structure contains a loop, allowing the network to capture dependencies in sequences. At each time step, the RNN receives the input and the hidden state of the previous time step and generates a new hidden state.
question:
The main problem of RNN is gradient disappearance and gradient explosion, making it difficult to learn long-term dependencies.
Long short-term memory network (LSTM)
structure:
LSTM is designed to solve the gradient problem of RNN. It introduces a memory cell to control the input, output and forgetting of information through a gate mechanism. The basic unit of LSTM contains a memory unit and three gates: forget gate, input gate and output gate.
Gating mechanism:
The forgetting gate decides what information to delete from the memory unit, the input gate decides what information to add, and the output gate decides what information to output.
Solving long-term dependency issues:
Through the gating mechanism, LSTM can handle long-term dependencies more effectively, allowing the network to remember or forget information when needed.
Application areas
RNN：
is used for short sequence problems, such as word sequences in natural language processing.
LSTM：
works better when processing sequence data that requires long-term memory, such as machine translation, speech recognition, stock price prediction, etc.
Summary comparison
Advantage:
LSTM is better at capturing and utilizing long-term dependencies in sequences than traditional RNN, and therefore performs better on certain tasks.
Disadvantages:
LSTM has higher computational cost, more model parameters, is relatively complex, and may require more computing resources.
Application options:
For simple sequence tasks, RNN may be sufficient. For tasks that require better handling of long-term dependencies, or for more complex sequential patterns, LSTM is a better choice.
In practical applications, LSTM is usually used as an improved version of RNN, especially when dealing with long sequences and long-term dependencies. However, with the development of research, some other sequence models, such as gated cyclic units (GRU), have also been widely used.
Support Vector Machines
Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression tasks. The main idea of SVM is to find a hyperplane that can separate samples of different categories while maximizing the distance from the hyperplane to the nearest sample point.
Main concepts
超平面（Hyperplane）：
In two-dimensional space, a hyperplane is a straight line; in three-dimensional space, it is a plane; and in higher-dimensional spaces, it is a hyperplane. SVM achieves classification by finding a hyperplane.
Support vectors (Support Vectors):
These are the closest data points to the hyperplane, and they are critical in defining the hyperplane and margin. Support vectors determine the final classification decision boundary.
Margin (Margin):
The margin refers to the shortest distance from the hyperplane to the support vector. The goal of SVM is to maximize this interval.
Kernel Function (Kernel Function):
SVM can use kernel functions to map input data to a high-dimensional space, making problems that are nonlinearly separable in the original space become linearly separable in the high-dimensional space.
Soft Margin:
When the data is not linearly separable, SVM can employ the concept of soft intervals, allowing some samples to lie inside the interval. This helps improve the generalization ability of the model.
SVMHow it works
Two classification problem:
SVM is mainly used to solve binary classification problems. By finding a hyperplane, the data is divided into two categories.
Maximum interval:
The goal of SVM is to find a hyperplane with a maximum margin so that the distance from the support vector to the hyperplane is maximized.
Kernel function:
For nonlinear problems, SVM uses kernel functions to map data to a higher-dimensional space, making the data easier to separate in the new space.
Regularization parameters:
SVM has a regularization parameter that can control the penalty for misclassification and adjust the complexity of the model.
Application areas
Image classification:
SVM can be used for image classification, such as face recognition.
Text Categorization:
In natural language processing, SVM is often used for text classification tasks, such as spam filtering, sentiment analysis, etc.
Bioinformatics:
SVM can be used in bioinformatics fields such as protein classification and gene expression data analysis.
Medical diagnosis:
SVM can be used for medical image processing and disease diagnosis, such as cancer detection.
The financial sector:
In the financial field, SVM can be used for tasks such as credit scoring and fraud detection.
SVM performs well in many fields, especially when the data dimension is high and the sample size is small. However, for large-scale data sets, training time may be longer. In recent years, with the rise of deep learning, SVM has been gradually replaced by deep learning methods in some fields.
Decision trees and random forests
Decision Tree and Random Forest are two commonly used models in machine learning, used for classification and regression tasks.
Decision Tree (Decision Tree)
basic concept:
A decision tree is a tree-like model used to make decisions about instances. Each non-leaf node represents an attribute test, each branch represents an output of the test result, and each leaf node represents a category or a predicted value.
Tree building process:
The tree building process of the decision tree is a recursive process. By selecting the best attributes for segmentation, the data in each child node is purer. The purity of an attribute is often evaluated using information entropy or the Gini index.
Features:
Decision trees are easy to understand and interpret, are insensitive to missing values, and can handle both numerical and categorical data. However, it is prone to overfitting and may be too sensitive to training data.
Zuiki Forest (Random Forest)
basic concept:
Random forest is an ensemble learning method that makes predictions by building multiple decision trees. Each decision tree is a base learner, ensembled by voting or averaging.
Modeling process:
Random forest models by bootstrap sampling the data set and randomly selecting features to increase the diversity of the model. The decision results of each tree are integrated in the final prediction.
Features:
Random forests usually have better generalization performance and have certain resistance to overfitting. It can handle a large number of input features and has good performance on high-dimensional data. It performs well in both classification and regression problems.
Application areas
Decision tree:
Decision trees are widely used in medical diagnosis, financial risk assessment, customer relationship management and other fields.
Random forest:
Random forests have achieved remarkable results in image recognition, text classification, bioinformatics and other fields. Due to its robustness and high performance, it is widely used to solve practical problems.
Summary comparison
Decision tree:
Simple and easy to understand, but prone to overfitting.
Random forest:
By integrating multiple decision trees, the generalization performance of the model is improved, and it can perform better on high-dimensional data and complex tasks.
In practical applications, random forests are often a powerful and effective tool, especially when high performance and robustness are required.
K nearest neighbor algorithm
K-Nearest Neighbors (KNN) is an instance-based learning algorithm used for classification and regression problems. The basic idea of KNN is: if most of the k nearest neighbor samples of a sample in the feature space belong to a certain category, then the sample also belongs to this category (for classification problems). For regression problems, KNN predicts based on the average or weighted average of the k nearest neighbor samples.
basic concept
Distance measure:
KNN usually uses Euclidean distance or Manhattan distance to measure the distance between samples.
Neighbor selection:
For a given sample, the k nearest neighbors are selected by calculating the distance to other samples.
Classification decision:
For classification problems, by counting the number of each category among k neighbors, the category with the largest number is selected as the prediction result.
Regression prediction:
For regression problems, the average or weighted average of the k nearest neighbors is used as the predicted value.
Selection of hyperparameterk:
The user needs to specify the value of k, which is an important hyperparameter. Methods such as cross-validation are usually used to select an appropriate k.
Features and applications
Non-parametric:
KNN is a non-parametric learning algorithm because it does not make explicit assumptions about the data.
Lazy to learn:
KNN belongs to lazy learning or instance-based learning. It does not perform an explicit training process on the data, but only performs calculations when prediction is needed.
Applicable fields:
KNN is suitable for small and medium-sized data sets, has no assumptions about the distribution of data, and is sensitive to outliers. It is widely used in image recognition, pattern recognition, recommendation systems and other fields.
Computational complexity:
As the number of samples increases, the computational complexity of KNN increases linearly, so it may not be suitable for large-scale data sets.
Advantages and Disadvantages
advantage:
- Simple and intuitive, easy to understand and implement.
- Suitable for multi-classification problems and situations where there are no clear assumptions about the data distribution.
shortcoming:
- The computational complexity is high for large-scale data sets.
- Sensitive to outliers and requires appropriate data preprocessing.
- The k value needs to be determined in advance, which will have a greater impact on the results.
KNN usually performs well in some simple classification and regression problems, but may not be the optimal choice when dealing with large-scale high-dimensional data. In practical applications, appropriate machine learning algorithms need to be selected based on the nature of the specific problem and the size of the data set.
Clustering Algorithm
Clustering algorithm is a type of unsupervised learning algorithm, which aims to divide the samples in the data set into different groups or clusters, so that the similarity of samples in the same group is high and the similarity between different groups is low. Clustering can help identify underlying patterns, group structures, or outliers in your data.
KK-Means ClusteringK-Means Clustering）
principle:
Divide the data into k clusters, each cluster represented by its centroid (the mean of the samples within the cluster). The algorithm iteratively optimizes the distance between a sample and the centroid of the cluster to which it belongs until convergence.
Features:
Simple, easy to understand, and better for large-scale data. But the effect may be poor for irregularly shaped clusters.
Hierarchical Clustering (Hierarchical Clustering):
principle:
Construct a hierarchical tree structure (clustering tree) based on the similarity between samples, and continuously merge or split clusters to Generate hierarchies.
Features:
There is no need to specify the number of clusters in advance, and the results show the similarity between data in a tree structure. But the computational complexity is high.
DBSCAN（Density-Based Spatial Clustering of Applications with Noise）：
principle:
Based on the density of the sample, high-density areas are divided into clusters and noise points can be identified. The core idea is to find a region around a sample where there are sufficiently dense samples.
Features:
has good robustness to irregularly shaped clusters and noise points, and there is no need to specify the number of clusters in advance.
谱聚类（Spectral Clustering）
principle:
Convert the data into spectral space, and achieve clustering of the original space by clustering the spectral space. Suitable for handling non-spherical clusters.
Features:
can handle non-convex shaped clusters and is widely used in image segmentation and other fields.
Gaussian Mixture Model, GMM ):
principle:
Assume that the data consists of multiple Gaussian distributions, and fit the data by estimating the parameters of each distribution. Each sample belongs to a certain distribution with a certain probability.
Features:
has a better fitting effect for complex data distributions and can estimate the probability that a data point belongs to each distribution.
Advantages and Considerations:
advantage:
Clustering algorithms are widely used in data mining, pattern recognition, image segmentation and other fields.
It can reveal the inner structure of data and help discover potential patterns and groups.
It is suitable for unsupervised learning scenarios and does not require pre-annotated category information.
Precautions:
Different clustering algorithms are suitable for different types of data and problems, and the appropriate algorithm needs to be selected according to the specific situation.
Clustering results may be affected by initial parameters and randomness, and it is recommended to run the algorithm multiple times to obtain stable results.
Care needs to be taken to select an appropriate distance measure or similarity measure to ensure the effectiveness of the algorithm.
reinforcement learning
Reinforcement Learning (RL) is a machine learning paradigm in which an agent learns to make decisions by interacting with the environment Decisions are made to maximize cumulative rewards. In reinforcement learning, an agent learns the dynamics of the environment by trying different actions and evaluates its behavior through reward signals. Here are some common reinforcement learning algorithms:
Q学习（Q-Learning）
principle:
Q learning is a reinforcement learning algorithm based on a value function. By learning an action value function (Q value function), the agent can choose Optimal actions to maximize cumulative rewards.
Features:
Q learning is model-based reinforcement learning and is suitable for discrete state and discrete action space problems.
深度Q网络（Deep Q Network，DQN）：
principle:
DQN is an algorithm that combines deep learning with Q-learning, using a deep neural network to estimate the Q-value function. It is capable of handling more complex state and action spaces.
Features:
DQN is suitable for processing high-dimensional continuous states and action spaces, and has better generalization performance. Experience replay and target network are introduced to improve the stability of the algorithm.
Policy Gradient MethodsPolicy Gradient Methods)
principle:
The policy gradient method directly learns the policy and generates actions through parameterized policy functions. Use gradient ascent to update policy parameters to increase cumulative rewards.
Features:
is suitable for continuous action space and high-dimensional state space. The strategy can be optimized directly, avoiding the estimation of value functions.
Actor-Critic Methods-Actor-Critic Methods)
principle:
The actor-critic method learns both policy and value functions simultaneously. Actors generate actions, and reviewers evaluate how good the actions are. The two are trained collaboratively through gradient descent.
Features:
combines the advantages of policy gradient and value function estimation, and can effectively handle high-dimensional continuous actions and state spaces.
Deep Deterministic Policy GradientDeep Deterministic Policy Gradient, DDPG)
principle:
DDPG is an algorithm for processing continuous action spaces that combines deep learning and policy gradient methods. It learns by approximating value functions and policy functions.
Features:
is suitable for continuous action space problems and has good convergence and stability.
Variations and improvements
TRPO（Trust Region Policy Optimization）：
By introducing constraints to keep the changes in each policy update within a controllable range, improve the stability of the algorithm.
PPO（Proximal Policy Optimization）：
Provides a simpler and more efficient policy gradient algorithm by overcoming some of the limitations of TRPO.
A3C（Asynchronous Advantage Actor-Critic）：
Improve the training efficiency of the algorithm by training multiple agents asynchronously.
Reinforcement learning has achieved remarkable results in many fields, such as gaming, robot control, autonomous driving, etc. Choosing an appropriate algorithm depends on the characteristics of the problem, including the nature of the state space, action space, and the reward structure of the task.
sparse coding
Sparse coding is a method for representing data. Its core idea is to represent the input data as a linear combination of a set of sparse basis vectors. In sparse coding, the coefficients of this set of basis vectors are usually obtained by solving optimization problems so that the representation has as high a sparsity as possible.
Fundamental
字典（Dictionary）：
Sparse coding uses a dictionary or a set of basis vectors to represent the input data. These basis vectors can be samples of the original data or can be obtained by other methods.
Sparsity:
The goal of sparse coding is to find a set of coefficients of the input data such that most elements in this set of coefficients are zero, thereby achieving a sparse linear combination of basis vectors.
Optimization:
Through optimization problems, L1 regularization (such as Lasso regularization) is usually used to promote sparse coefficients. The goal of the optimization problem is to minimize the representation error and L1 regularization term.
Application areas
Image Processing:
Sparse coding is widely used in image compression, denoising and feature extraction. Local patches in an image can be represented as sparse linear combinations in a dictionary.
Voice signal processing:
In speech signal processing, sparse coding can be used for audio compression, speech recognition and other tasks to represent the speech signal as a linear combination of basis vectors.
Signal processing and communications:
In the fields of signal processing and communications, sparse coding can be used for signal compression and sparse signal reconstruction, such as through compressed sensing techniques.
Feature learning:
Sparse coding is also commonly used to learn compact representations of data for feature learning and pattern recognition.
Sparse coding and deep learning
Autoencoder:
In deep learning, an autoencoder is a model related to sparse coding that achieves data dimensionality reduction and feature learning by learning a compact representation of the input data.
Dictionary learning:
Dictionary learning is a concept closely related to sparse coding, which aims to learn a dictionary (a set of basis vectors) of the data to achieve a compact representation of the data.
Sparse coding has made significant progress in the fields of signal processing and machine learning in the past few years. It not only provides an efficient data representation method, but also shows strong performance in many applications. In practical applications, it is very important to select appropriate dictionaries and optimization methods based on the characteristics of the specific problem and the nature of the data.
Bayesian network
Bayesian Network (Bayesian Network, BN), also known as belief network (< /span>) or probabilistic graphical model, is a graphical model used to model probabilistic relationships between random variables. Bayesian networks are based on Bayes' theorem and are able to represent and infer probabilistic dependencies between variables. Belief Network
Main features and principles
Directed Wireless Encyclopedia (DAG):
Bayesian network is a directed acyclic graph. The nodes in the graph represent random variables and the directed edges represent the dependence between variables. relation.
Conditional independence:
Bayesian networks simplify complex probability distributions through the assumption of conditional independence. Given a network structure, all non-direct descendant nodes of a node in the graph are conditionally independent, given its parent node.
Probability distributions:
Describe the relationship between variables through joint probability distribution and conditional probability distribution. Each node represents the probability distribution of a variable, and the conditional probability distribution represents the conditional probability given its parent node.
Network parameters:
The parameters of the Bayesian network include the probability table of the node and the conditional probability table of each directed edge.
Modeling and Application
Model complex systems:
Bayesian networks are often used to model uncertainty and probabilistic relationships in complex systems, such as medical diagnosis, financial risk assessment, and natural language processing and other fields.
policy support:
Bayesian networks can be used in decision support systems to help analyze and evaluate the risks and uncertainties of decisions.
Inference and Prediction:
Bayesian network can be used to reason about the probability distribution of unknown variables and make predictions and decisions.
Troubleshooting:
In engineering and electronic systems, Bayesian networks are often used for fault diagnosis to help analyze fault propagation and repair strategies in the system.
Bayesian network inference method
Accurate inference:
Accurate probability distributions can be obtained through precise methods, such as variable elimination and clique tree.
Approximate inference:
For large-scale networks or difficult-to-handle situations, use approximate inference methods such as Markov Chain Monte Carlo (MCMC) and variational infer.
Bayesian network tools and software
BayesiaLab：
A commercial software for Bayesian network modeling and analysis.
GeNIe和SMILE：
Free software for building, editing, and analyzing Bayesian networks developed by Decision Systems Laboratory.
PyMC3sumStan:
Two libraries for probabilistic programming in Python, supporting the construction of Bayesian networks and probabilistic inference.
Bayesian networks have strong expressive ability and practicality in facing uncertainty and probabilistic relationship modeling problems. It is a type of probabilistic graphical model that, along with other models such as Markov random fields, is widely used in the fields of machine learning and artificial intelligence.